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CommonAI CIC is a non-profit organisation built around collaborative engineering and is backed by a mix of UK Government and private funding in order to design, build and deploy innovative AI systems. CommonAI CIC is a non-profit organisation built around collaborative engineering and is backed by a mix of UK Government and private funding in order to design, build and deploy innovative AI systems. Our Scaling Inference Lab (SIL) is developing simulation grounded in real measurements to help the UK invest in promising AI infrastructure. We connect proposed hardware designs to realistic workloads, helping investors and engineers understand their potential, their limitations and the next experiment worth doing. Build simulation software that helps engineers and investors explore how AI systems would behave with different processors, memory systems, networks or serving strategies. Your work will help turn models and existing software into a practical framework for comparing design choices against realistic LLM workloads. You will develop and connect simulation components, making sure that changes in one part of a system have the appropriate consequences elsewhere. This means understanding unfamiliar code, designing clear interfaces and building software whose predictions can be investigated and tested against measurements. We aim to reuse and extend existing frameworks wherever they provide a sound foundation. We welcome depth in one or more relevant areas, including simulation, systems software, performance modelling and inference serving. Your initial focus will reflect your strengths and the components we choose to build on, with complementary expertise added as the team grows. • Develop and integrate simulation components that capture behaviour and interactions relevant to an AI infrastructure comparison, • Work with the technical lead and our partners to evaluate existing frameworks and software, extending and connecting useful components through clear interfaces, • Develop appropriate models of system state and resource use so that changes in one component have the appropriate consequences elsewhere, • Build reproducible simulation scenarios, diagnostic outputs and tests that expose incorrect assumptions, integration errors and unsupported cases, • Work with measurement and uncertainty colleagues to compare predictions with evidence, improve models and integrate calibration and uncertainty methods into the simulator, • Maintain reliable software, documentation and development workflows, using AI coding agents to support implementation and testing An example early project would be to bridge an existing simulator component with our workload models and measurement datasets, producing a reproducible comparison between predicted and measured performance over two systems. • Strong software engineering skills and evidence of building, debugging and maintaining substantial technical software, • Experience in one or more of: discrete-event simulation; computer architecture or performance modelling; distributed/HPC systems; inference runtimes and serving; memory or network modelling; integration of scientific software and heterogeneous components, • The ability to understand unfamiliar code, reason about interfaces and assess the suitability and maintenance costs of reusing, extending or building software, • A collaborative approach to testing assumptions and working with specialists in adjacent areas, • Practical experience using AI coding agents, with the judgement to check their output for correctness and maintainability Python, C++, Rust or other relevant systems or scientific programming experience can provide a route into the role. Familiarity with vLLM or SGLang, simulator frameworks, or hardware performance tools would be useful. Depth in a subset of these areas is sufficient; languages and frameworks will follow the engineering needs of the programme. • Help build UK AI capability and improve how investment and compute serve the public good, • Build a network spanning UK hardware SMEs, major technology companies, venture and public investors, and leading universities, • Shape a growing team with startup pace and close academic links, • Use frontier models and coding agents with a substantial tooling budget, • A collaborative and supportive work environment, • The opportunity to have a high impact in a growing organisation, • Competitive salary package, including pension and benefits, • Professional development opportunities, • Networking opportunities with influential figures from across the tech sector and academia, • A vibrant office environment located a few minutes' walk away from Cambridge train station Hybrid, with Cambridge attendance agreed individually around collaboration and the work; applicants across the UK are welcome. We are open to permanent employment, part-time arrangements and consultancy. We would like you to start as soon as practical and can be flexible for the right person. We particularly welcome experienced engineers and can also consider earlier-career candidates with strong evidence of ability. Relevant postgraduate study is useful, but qualifications are secondary to experience and the quality of your work. CommonAI CIC is an equal opportunity employer and is committed to creating an inclusive and diverse workplace. #J-18808-Ljbffr

CommonAI CIC is a non-profit membership organisation, founded on a belief in collaborative engineering for the safe and responsible development of foundational AI technologies. A place where AI startups, enterprises large and small, public sector bodies and academia can share resources and knowledge, to codevelop and grow businesses, fast. We are led by experienced founders, investors and engineers who believe that collaborative engineering drives faster AI innovation and are backed by a mix of UK Government and private funding in order to design, build and deploy innovative AI systems. We are seeking an AI Security Researcher with the mindset of an attacker: someone who enjoys finding out how systems can be broken, misused or circumvented. Our research team works on reducing the complexity of large language models while retaining their performance. You will support our High Assurance programme, which works on the accuracy, explainability and accountability of AI systems used in regulated environments such as financial services. You will track the research that matters to the programme, challenge its technical decisions, and help test whether the simpler, more interpretable models emerging from our frontier research are easier to audit in practice. • Track key research and developments relevant to the High Assurance programme, including attacks on AI models, defences against them, and the security of the systems that serve them., • Report on what these developments mean for the programme and our Members, through written briefings and presentations., • Review the programme's technical decisions and directions from an attacker's point of view, and propose stronger alternatives., • Threat-model AI systems used in regulated settings, covering model weights, training data, inference serving, and the tools and agents built on them., • Red-team the systems we build, within an agreed scope, and report findings through a clear disclosure process., • Help develop interpretability methods for the simpler models produced by our frontier research, and test their limits, for example by trying to hide behaviour the methods should detect., • Build internal expertise by sharing what you learn with colleagues and helping answer technical questions from our Members., • Proven experience in security research, offensive security or adversarial machine learning (e.g. red-teaming, penetration testing, vulnerability research, or attacks on ML models)., • A strong understanding of how modern AI systems work, including how large language models are trained, deployed and served., • Familiarity with attacks on AI systems (e.g. jailbreaks, prompt injection, data poisoning, backdoors, model extraction)., • Strong Python, and the ability to build your own tools and experiments., • Excellent written communication: you can turn complex technical findings into clear briefings for technical and non-technical readers., • The judgement and confidence to challenge technical decisions constructively., • A strong enthusiasm for using LLMs, coding assistants, and agents as core tools in your own software development process., • Experience with interpretability methods (e.g. probing, sparse autoencoders, circuit analysis), or a strong interest in learning them., • Experience working on software systems that operate in highly regulated or high-assurance environments (e.g. financial services)., • An understanding of the latest AI safety research and active involvement in that community., • Knowledge of hardware and side-channel security (e.g. timing attacks, risks in shared GPU and inference infrastructure)., • Familiarity with AI assurance standards and regulation (e.g. ISO/IEC 42001, NIST AI RMF, the EU AI Act)., • A public track record, such as published research, CVEs, conference talks, bug bounties or CTF results., • A PhD in security, machine learning or a related field, or equivalent research experience., • A collaborative and supportive work environment, • The opportunity to have a high impact in a growing organisation, • Competitive salary package and pension, • Professional development opportunities, • Networking opportunities with influential people from across the tech sector, financial services, and academia, • A vibrant office environment located a few minutes' walk away from Cambridge train station CommonAI CIC is an equal opportunity employer and is committed to creating an inclusive and diverse workplace. #J-18808-Ljbffr

Healx is hiring a motivated Platform Engineer to build cloud infrastructure and develop production pipelines that enable our AI drug discovery process. You will bridge the gap between infrastructure and scientific software, designing, building and supporting cloud infrastructure while collaborating with bioinformaticians, machine learning engineers and data scientists to implement data pipelines. You will join a small, agile engineering team where individuals take ownership of tasks, innovate quickly and collaborate extensively through pair programming. Working closely with technical members of data and digital science teams you will clearly see the impact your work has on pioneering the next generation of AI drug discovery and finding treatments for rare disease patients in need. We encourage you to provide a covering letter to explain why you think you would be successful in this role. Our engineers understand and use AI development tools to solve problems and implement systems efficiently while retaining responsibility for system design, implementation and testing of code. We aim to move quickly while building systems we understand deeply and can maintain. Healx is an AI-powered tech bio company that is redesigning drug discovery. With 10,000 rare diseases affecting 400 million people globally, 90% of which have no approved treatment, Healx is on a mission to pioneer the next generation of drug discovery to help rare disease patients in need. We combine data, artificial intelligence and deep pharmacology expertise to develop treatments more quickly and cheaply than traditional drug discovery. Diversity and inclusion sits at the heart of our mission to help people with rare diseases, and we believe that attracting and empowering a diverse team is critical to achieving this goal. We welcome applications from people from all backgrounds and walks of life. Below we have included the qualities that we feel are required for you to excel in this role; however we appreciate that people can apply transferable skills from all walks of life and experience. If you think you have what it takes, love our mission and resonate with our values but are worried you don't tick every box - we still want to hear from you and encourage you to apply! • Design, build, maintain and improve cloud infrastructure in AWS (and some GCP) to enable AI drug discovery workflows, • Follow infrastructure as code (IaC) and CI/CD principles to build maintainable infrastructure using Terraform and CircleCI, • Take advantage of latest AWS tools and apply best practices across our cloud estate to ensure security, availability and observability, • Work closely with technical users across the company to understand and support their computational needs, • In collaboration with data scientists write code to build, test and automate production pipelines in tools such as Nextflow, Airflow, MLflow and KNIME, • Embrace the use of AI coding tools and agents to accelerate delivery while retaining responsibility for system design, implementation and testing of code, • You have strong ownership of parts of our cloud infrastructure, familiarity with our Terraform codebase and are shipping regular improvements, • Infrastructure you've designed and deployed is supporting drug discovery pipelines, • You have taken ownership of security, cost management and observability in our cloud estate, • Computational workflows are in production that you've built in Nextflow, Airflow, MLflow or similar in collaboration with digital scientists, • You have shipped maintainable code to enable lambda functions, automation scripts and internal tools in several of our Python repositories, • We're looking for a mid level engineer with solid hands on experience building cloud infrastructure in a commercial environment who is keen to apply what they already know and learn new skills, • You are highly collaborative and driven to solve problems that help others and drive the Healx mission, • You have hands-on experience (typically around 2+ years) working with a cloud platform, ideally AWS, and are excited about building cloud infrastructure following best practices, • You are a proponent and practitioner of infrastructure as code principles, ideally with experience in Terraform, and have experience building, maintaining and scaling CI/CD pipelines, • Solid experience writing maintainable and well tested code following best practices in at least one language (we use mainly Python and some Go), • An automation mindset, ideally with experience building production workflows in tools like Airflow Healx operates from a modern, accessible office in central Cambridge, within easy reach of the train station. We offer a flexible, diverse and inclusive working environment that supports individual needs and promotes a sustainable work-life balance, and we are open to discussing flexible working arrangements. We are a hybrid team that works in a highly collaborative model, valuing synchronisation and pair programming. If you are excited about the role but unsure whether your location aligns perfectly, we encourage you to apply and start a conversation. Remote employees are expected to attend workshops in Cambridge every couple of months. You will join colleagues with decades of combined experience who are passionate about collaboration and supporting each other’s growth. The team is committed to helping you develop your skills and support your personal and career development. Healx provides the guidance and support you need to do your best work and make an impact. If you require any reasonable adjustments during the recruitment process, please let us know — we’re happy to accommodate. • Care for Rare – Rare disease patients are at the heart of what we do, • Grow as individuals – We are learners always seeking to enhance our expertise, • Win as a team – We strive to remain inclusive and diverse and we celebrate successes and lessons together, • Innovate and deliver – Our mission requires rapid innovation and calculated risks that won’t compromise our high standards, • Financial – Competitive salary, share options, 7% employer pension contributions, life insurance of 4x base salary, • Health and Wellbeing – Private medical insurance, 25 days annual leave (plus bank holidays) with the option to purchase additional days to support a healthy work‑life balance, wellbeing support via Spill and our Employee Assistance Programme, • Hybrid Working – Flexible and remote working options, home office set‑up allowance, periodic in‑person team days for company‑wide collaboration and celebration, • Family Friendly – Enhanced family leave policies and flexible working practices, • Personal Development and Growth – Personal learning and development budgets, regular personal development conversations and career support, • Community Engagement and Support – One paid day off per year to volunteer for a cause aligned with our mission of supporting patients living with rare diseases; the opportunity to hear from and engage with patient groups and communities who offer us valuable insights into the experiences of those affected by rare diseases We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us. #J-18808-Ljbffr

CommonAI CIC is a non-profit membership organisation, founded on a belief in collaborative engineering for the safe and responsible development of foundational AI technologies. A place where AI startups, enterprises large and small, public sector bodies and academia can share resources and knowledge, to codevelop and grow businesses, fast. We support technology-focused start ups, each with unique data management challenges, and are seeking an experienced Storage Architect to help them design, deploy and maintain high-performance storage systems for their AI and data-driven workloads. The successful candidate will combine deep experience architecting and managing distributed, cloud, and tiered storage solutions with strong Linux and automation skills. In this role you will: • Design, implement, and maintain storage platforms that support large-scale AI and data pipelines, • Manage distributed storage systems such as Ceph, Lustre, or BeeGFS., • Oversee tiered storage architectures, optimizing data movement across high-performance, object, and archival tiers., • Ensure data integrity, availability, and security across on-premises and cloud environments., • Develop automation and monitoring tools using Bash, Python, or similar scripting languages., • Manage and secure container images and related storage used for AI and ML workloads., • Integrate storage systems with public cloud services (AWS, Azure, GCP) and hybrid environments., • Troubleshoot complex storage and data flow issues, collaborating closely with AI platform and infrastructure teams., • Contribute to ongoing architecture improvements, performance tuning, and capacity planning. To be considered candidates should meet most of the following requirements: • Strong Linux system administration background., • Proven experience installing, configuring, and maintaining Ceph clusters or similar technologies in a production environment., • Familiarity with distributed filesystems (e.g., Lustre, BeeGFS) and cloud-based storage services (e.g. EC2)., • Experience with tiered storage management and lifecycle data policies., • Scripting and automation proficiency (e.g. Bash, Python, Terraform/OpenTofu, Ansible)., • Understanding of data security best practices and compliance considerations., • Experience working with container technologies (e.g. Docker, Kubernetes) and image storage registries., • Strong analytical, troubleshooting, communication and documentation skills. We also value: • Knowledge of GPU compute environments or AI training infrastructure., • Experience with monitoring and observability tools (Prometheus, Grafana, etc.)., • Contributions to open-source storage, data management, or infrastructure projects., • Familiarity with object storage systems (S3, RADOS Gateway, MinIO, etc.)., • A collaborative and supportive work environment., • The opportunity to have a high impact in a growing organisation., • Competitive salary package and pension., • Professional development opportunities., • Networking opportunities with influential people from across the tech sector and academia., • A vibrant office environment located a few minutes walk away from Cambridge train station. CommonAI CIC is an equal opportunity employer and is committed to creating an inclusive and diverse workplace. #J-18808-Ljbffr

CommonAI CIC is a non-profit organisation built around collaborative engineering and is backed by a mix of UK Government and private funding in order to design, build and deploy innovative AI systems. Our Scaling Inference Lab (SIL) is developing simulation grounded in real measurements to help the UK invest in promising AI infrastructure. We connect proposed hardware designs to realistic workloads, helping investors and engineers understand their potential, their limitations and the next experiment worth doing. Help evaluate proposed AI hardware before it is available as a finished chip. You will turn RTL designs into working FPGA prototypes and emulation experiments, giving SIL and its technology partners a practical way to explore design behaviour and architectural choices. The initial focus is on RTL prototyping and emulation. You will help choose a useful first design and platform, adapt and integrate the RTL, and establish repeatable build, bring-up and experimental workflows. Working with our partners and colleagues in software and simulation, you will connect observations from the prototypes to wider system studies. Your work will help establish what the experiments tell us about a proposed design, how those findings relate to simulator models and where further evidence is needed. • Adapt and integrate RTL for FPGA prototyping or emulation, working with the design owners and documenting changes needed for the prototype., • Develop and maintain synthesis, implementation, timing-closure and reproducible build flows for the selected platform., • Bring up and debug designs, developing tests and instrumentation to check functional behaviour and support repeatable experiments., • Develop or integrate the control, stimulus and data-capture facilities needed for the agreed experiments, working with software colleagues on host interfaces and integration., • Relate observations from emulation to the model being evaluated, explaining how prototype frequency, interface overheads and other limitations affect interpretation., • Help assess platform, toolchain and hardware requirements, and document workflows that colleagues and partners can reuse., • An example early project would be to take a partner's RTL design through FPGA implementation and bring-up, then instrument it to investigate a selected architectural choice and compare observations with a simulator model., • Hands-on experience taking RTL through an FPGA prototyping or emulation flow and debugging the resulting system., • Strong digital-design skills in Verilog, SystemVerilog, VHDL or an equivalent HDL, together with practical timing and verification judgement., • Experience with a relevant FPGA or emulation toolchain and the scripting needed to make builds and experiments repeatable., • The ability to collaborate across hardware and software boundaries and explain the conclusions and limitations of a prototype experiment., • Practical experience using AI coding agents, with the judgement to validate generated HDL, scripts and software., • Experience with processor or accelerator designs, multi-FPGA partitioning, host interfaces or performance characterisation would be useful. Depth with a relevant platform is sufficient; the team will choose the initial platform and design around the first useful study., • Help build UK AI capability and improve how investment and compute serve the public good., • Build a network spanning UK hardware SMEs, major technology companies, venture and public investors, and leading universities., • Shape a growing team with startup pace and close academic links., • Use frontier models and coding agents with a substantial tooling budget., • A collaborative and supportive work environment, • The opportunity to have a high impact in a growing organisation, • Competitive salary package, including pension and benefits, • Professional development opportunities, • Networking opportunities with influential figures from across the tech sector and academia, • A vibrant office environment located a few minutes' walk away from Cambridge train station Hybrid, with Cambridge attendance agreed individually around collaboration and the work; applicants across the UK are welcome. We are open to permanent employment, part-time arrangements and consultancy. We would like you to start as soon as practical and can be flexible for the right person. We particularly welcome experienced engineers and can also consider earlier-career candidates with strong evidence of ability. Relevant postgraduate study is useful, but qualifications are secondary to experience and the quality of your work. CommonAI CIC is an equal opportunity employer and is committed to creating an inclusive and diverse workplace. #J-18808-Ljbffr

CommonAI CIC is a non-profit organisation built around collaborative engineering and is backed by a mix of UK Government and private funding in order to design, build and deploy innovative AI systems. Our Scaling Inference Lab (SIL) is developing simulation grounded in real measurements to help the UK invest in promising AI infrastructure. We connect proposed hardware designs to realistic workloads, helping investors and engineers understand their potential, their limitations and the next experiment worth doing. Help evaluate proposed AI hardware before it is available as a finished chip. You will turn RTL designs into working FPGA prototypes and emulation experiments, giving SIL and its technology partners a practical way to explore design behaviour and architectural choices. The initial focus is on RTL prototyping and emulation. You will help choose a useful first design and platform, adapt and integrate the RTL, and establish repeatable build, bring-up and experimental workflows. Working with our partners and colleagues in software and simulation, you will connect observations from the prototypes to wider system studies. Your work will help establish what the experiments tell us about a proposed design, how those findings relate to simulator models and where further evidence is needed. • Adapt and integrate RTL for FPGA prototyping or emulation, working with the design owners and documenting changes needed for the prototype., • Develop and maintain synthesis, implementation, timing-closure and reproducible build flows for the selected platform., • Bring up and debug designs, developing tests and instrumentation to check functional behaviour and support repeatable experiments., • Develop or integrate the control, stimulus and data-capture facilities needed for the agreed experiments, working with software colleagues on host interfaces and integration., • Relate observations from emulation to the model being evaluated, explaining how prototype frequency, interface overheads and other limitations affect interpretation., • Help assess platform, toolchain and hardware requirements, and document workflows that colleagues and partners can reuse. An example early project would be to take a partner's RTL design through FPGA implementation and bring-up, then instrument it to investigate a selected architectural choice and compare observations with a simulator model. • Hands-on experience taking RTL through an FPGA prototyping or emulation flow and debugging the resulting system., • Strong digital-design skills in Verilog, SystemVerilog, VHDL or an equivalent HDL, together with practical timing and verification judgement., • Experience with a relevant FPGA or emulation toolchain and the scripting needed to make builds and experiments repeatable., • The ability to collaborate across hardware and software boundaries and explain the conclusions and limitations of a prototype experiment., • Practical experience using AI coding agents, with the judgement to validate generated HDL, scripts and software., • Help build UK AI capability and improve how investment and compute serve the public good., • Build a network spanning UK hardware SMEs, major technology companies, venture and public investors, and leading universities., • Shape a growing team with startup pace and close academic links., • Use frontier models and coding agents with a substantial tooling budget., • A collaborative and supportive work environment, • The opportunity to have a high impact in a growing organisation, • Competitive salary package, including pension and benefits, • Professional development opportunities, • Networking opportunities with influential figures from across the tech sector and academia, • A vibrant office environment located a few minutes’ walk away from Cambridge train station Hybrid, with Cambridge attendance agreed individually around collaboration and the work; applicants across the UK are welcome. We are open to permanent employment, part-time arrangements and consultancy. We would like you to start as soon as practical and can be flexible for the right person. We particularly welcome experienced engineers and can also consider earlier‑career candidates with strong evidence of ability. Relevant postgraduate study is useful, but qualifications are secondary to experience and the quality of your work. CommonAI CIC is an equal opportunity employer and is committed to creating an inclusive and diverse workplace. #J-18808-Ljbffr

CommonAI CIC is a non-profit organisation built around collaborative engineering and is backed by a mix of UK Government and private funding in order to design, build and deploy innovative AI systems. Our Scaling Inference Lab (SIL) is developing simulation grounded in real measurements to help the UK invest in promising AI infrastructure. We connect proposed hardware designs to realistic workloads, helping investors and engineers understand their potential, their limitations and the next experiment worth doing. Lead the engineering team building a simulation platform for AI inference systems. You will bring together realistic workloads, hardware models and physical measurements to predict system performance and make the uncertainty in those predictions explicit. The platform will help engineers and investors assess emerging AI infrastructure, understand design choices, and identify next experiments. You will turn scientific direction into an implementable architecture and delivery plan, help shape recruitment, and develop a team with complementary expertise across software, modelling, measurement and hardware. You will report to the SIL Scientific Director and line manage a group of engineers in the growing SIL team. Working closely with the Director of Engineering, you will provide daily technical leadership and help shape the team as we recruit. Technical judgement and the ability to bring people with you matter more than having held a particular management title in the past. • Own the simulator codebase, working closely with the Scientific Director, SIL on architectural decisions. Translate the programme's scientific goals into an achievable engineering roadmap, • Assess existing simulators, serving software and partner components; decide what to reuse, extend or build, and establish interfaces that make the pieces work together, • Shape recruitment, assess candidates, mentor colleagues and line-manage engineers, building complementary expertise across software, measurement, modelling and hardware, • Ensure simulator predictions are tested against physical measurements and realistic LLM workloads, with uncertainty made explicit to the people interpreting the results, • Establish engineering practices for maintainable software, testing, reproducible experiments and effective use of AI coding agents; review designs and results with the relevant specialists, • Contribute directly to implementation and debugging when engineering coverage is incomplete or a colleague needs support An example early project would be to lead a complete study from an investor's question through simulation and measured validation to a useful evidence report, while establishing the team and foundations for subsequent cases. • Substantial industrial engineering experience delivering complex software or hardware/software systems, with clear examples of technical decisions you have owned, • Depth in at least one of systems simulation, computer architecture, distributed or HPC systems, inference serving, or experimental performance engineering. Experience across more than one is especially valuable, • Evidence that you can lead people through difficult technical work, explain trade-offs and develop others. This may come from technical leadership and mentoring as well as formal management, • The judgement to turn high-level scientific direction into independent engineering decisions, and to recognise when wider input is needed, • Practical engineering ability to review code and designs, investigate failures and help colleagues resolve implementation difficulties, • Experience using AI coding agents, with the ability to assess their output and help colleagues use them effectively Experience connecting research prototypes to maintained engineering systems, collaborating with universities or technology suppliers, and comparing models with measurements would be valuable. We do not expect you to be the deepest specialist in every part of the programme. • Help build UK AI capability and improve how investment and compute serve the public good, • Build a network spanning UK hardware SMEs, major technology companies, venture and public investors, and leading universities, • Shape a growing team with startup pace and close academic links, • Use frontier models and coding agents with a substantial tooling budget, • A collaborative and supportive work environment, • The opportunity to have a high impact in a growing organisation, • Competitive salary package, including pension and benefits, • Professional development opportunities, • Networking opportunities with influential figures from across the tech sector and academia, • A vibrant office environment located a few minutes' walk away from Cambridge train station Hybrid working, with Cambridge attendance agreed individually around collaboration and job need. We would like you to start as soon as practical and can be flexible for the right person. We value engineering quality and demonstrated leadership over a particular qualification or previous management title. CommonAI CIC is an equal opportunity employer and is committed to creating an inclusive and diverse workplace. #J-18808-Ljbffr

CommonAI CIC is a non-profit membership organisation, founded on a belief in collaborative engineering for the safe and responsible development of foundational AI technologies. A place where AI startups, enterprises large and small, public sector bodies and academia can share resources and knowledge, to codevelop and grow businesses, fast. We are led by experienced founders, investors and engineers who believe that collaborative engineering drives faster AI innovation and are backed by a mix of UK Government and private funding in order to design, build and deploy innovative AI systems. We are seeking a Senior Software Engineer with a passion for open-source AI infrastructure to work on deploying, extending and optimising vLLM (and potentially other inference serving engines) to support our projects. You will play a crucial, high-impact role across both our key programmes, the Scaling Inference Lab () and the High Assurance programme. As an AI-first company, we strongly believe in collaborative engineering powered by the tools we are helping to build. This role places a major emphasis on using LLMs, AI coding assistants, and autonomous agents for software development. • Deploy, instrument and monitor open weight models served using vLLM., • Implement new features within vLLM to support novel hardware architectures as part of the Scaling Inference Lab., • Work with the Panopticon team to identify opportunities to extend vLLM to enhance accuracy, explainability, and accountability when using it to serve models in regulated environments., • Actively collaborate with the open-source vLLM community to propose, review, and upstream core changes., • Troubleshoot, profile, and optimise inference performance, focusing on latency, throughput, and hardware utilisation., • Proven experience working as a Senior Software Engineer with deep expertise in both Python and low-level programming (e.g. C/C++, Rust, assembly or CUDA)., • A history of direct contributions to vLLM or similar high-performance open-source ML/AI projects (e.g. PyTorch, Hugging Face TGI, TensorRT-LLM, Ray)., • Strong understanding of LLM inference mechanics (e.g. KV caching, continuous batching, memory management, model quantisation)., • Experience interacting with, and upstreaming code to, active open-source communities., • Hands-on experience working on performance optimisation for hardware accelerators (GPUs, TPUs, CPU vector units or other accelerators)., • A strong enthusiasm for using LLMs, coding assistants, and agents as core tools in your own software development process., • Experience working on software systems that operate in highly regulated or high-assurance environments (e.g. financial services)., • An understanding of the latest AI safety research and active involvement in that community., • Deep knowledge of modern MLOps practices, CI/CD, and large-scale deployments., • A collaborative and supportive work environment, • The opportunity to have a high impact in a growing organisation, • Competitive salary package and pension, • Professional development opportunities, • Networking opportunities with influential people from across the tech sector, financial services, and academia, • A vibrant office environment located a few minutes' walk away from Cambridge train station CommonAI CIC is an equal opportunity employer and is committed to creating an inclusive and diverse workplace. #J-18808-Ljbffr

CommonAI CIC is a non-profit organisation built around collaborative engineering and is backed by a mix of UK Government and private funding in order to design, build and deploy innovative AI systems. Our Scaling Inference Lab (SIL) is developing simulation grounded in real measurements to help the UK invest in promising AI infrastructure. We connect proposed hardware designs to realistic workloads, helping investors and engineers understand their potential, their limitations and the next experiment worth doing. Make uncertainty a first-class output of an AI systems simulator. When a simulation predicts that one design will outperform another, you will help establish how much confidence the evidence supports, which assumptions could change the conclusion, and which experiments would reduce the uncertainty that matters to the decision. You will combine mathematical reasoning with practical software engineering to calibrate models against measurements, test predictions on independent cases and build uncertainty analysis into the simulation workflow. Your methods and software will help engineers and investors understand what the simulator predicts and the limits of those predictions. We welcome people from AI systems, HPC and other fields where simulation is tested against experiments. You might bring systems engineering experience and an interest in developing uncertainty methods, or experience in mathematical modelling and scientific computing that you want to apply to AI infrastructure. • Develop and implement methods to calibrate simulator models against measurements and evaluate predictions on cases that were not used for calibration., • Distinguish uncertainty arising from measurements, model assumptions and parameters, operational variation and limited simulation runs, and represent it appropriately., • Analyse sensitivity and uncertainty in comparisons between alternatives, accounting for shared sources of uncertainty and their effects on the comparison., • Develop outputs that show how uncertain a prediction is, when a ranking could change, and when a result extends beyond the evidence used to validate it., • Work with measurement and workload engineers to design experiments that test modelling assumptions and reduce uncertainty that matters to the decision., • Integrate these methods into repeatable simulation workflows and communicate their assumptions and limitations to engineers and the technical analyst., • An example early project might be to establish a calibration and validation workflow for the a comparison between two architectures, with uncertainty outputs that help explain the result. You will develop and review the methodology with the technical lead and Scientific Director, SIL., • Mathematical maturity: the ability to reason carefully about models, data, variation and the assumptions behind a quantitative claim., • Software engineering experience sufficient to turn numerical methods or analysis into readable, tested, reusable tools., • Relevant experience in one or more of: quantitative systems performance analysis, statistical modelling, simulation validation, uncertainty quantification, scientific/HPC software or the theory of experimental design., • The ability to collaborate with specialists and explain both a result and the limits of the evidence supporting it., • Practical experience using AI coding agents and checking their output., • Experience with model discrepancy, sensitivity analysis, Bayesian methods, surrogate models or sequential experimental design would be useful. So would familiarity with computer architecture or inference systems. We welcome complementary backgrounds: strong systems engineers developing uncertainty expertise, and engineers with modelling or uncertainty expertise moving into AI infrastructure., • Help build UK AI capability and improve how investment and compute serve the public good., • Build a network spanning UK hardware SMEs, major technology companies, venture and public investors, and leading universities., • Shape a growing team with startup pace and close academic links., • Use frontier models and coding agents with a substantial tooling budget., • A collaborative and supportive work environment, • The opportunity to have a high impact in a growing organisation, • Competitive salary package, including pension and benefits, • Professional development opportunities, • Networking opportunities with influential figures from across the tech sector and academia, • A vibrant office environment located a few minutes’ walk away from Cambridge train station Hybrid, with Cambridge attendance agreed individually around collaboration and the work; applicants across the UK are welcome. We are open to permanent employment, part-time arrangements and consultancy. We would like you to start as soon as practical and can be flexible for the right person. We particularly welcome experienced engineers and can also consider earlier-career candidates with strong evidence of ability. Relevant postgraduate study is useful, but qualifications are secondary to experience and the quality of your work. #J-18808-Ljbffr

Design, build and keep evolving the physical networks that connect the clusters in the Scaling Inference Lab (SIL). The SIL, backed by ARIA, is a neutral, UK-based testbed where new AI technologies (accelerators, memory systems and interconnects) are tested at rack scale against real workloads for the very first time. The results help UK startups, investors and public compute programmes decide what to build, fund and buy, and they build lasting UK capability in the infrastructure that AI depends on. The network is critical to every experiment and this role will own the cabling, switch layouts and topologies for the out-of-band, frontend and accelerator fabrics in our lab at Kao Data in Harlow, and in any further facilities we establish in the UK. The SIL is an R&D facility and its requirements evolve rapidly. A new cluster might need a rail-optimised InfiniBand fabric, an 800G Ethernet frontend or an optical circuit-switched interconnect that has never been deployed at this scale. An older cluster might need re-cabling so its equipment can be reused in the next one. You will gather requirements from members, CommonAI staff and other key stakeholders to create flexible and resilient network designs. In this role you will work closely with both technical staff and the project management team to provide the detailed designs that suppliers, cabling contractors and installers need. • Design the physical network for each new cluster across out-of-band, frontend/storage and accelerator fabrics: topology, switch placement, port maps, cable schedules and bills of materials., • Specify fibre and copper infrastructure in detail, including fibre grade, connectors, polarity, pinning, trunk and cassette layouts, patch-panel arrangements and end-to-end loss budgets, and select matching transceivers, DACs and AOCs., • Plan changes to existing clusters so equipment can be re-cabled and reused with minimal disruption to running work., • Act as the technical authority for suppliers and installers: produce drawings and specifications, review quotations, and inspect and test completed work., • Maintain accurate, well-labelled as-built documentation of every rack, cable, port and link., • Work with project managers on sequencing and lead times, and with operators and software colleagues on bring-up and fault-finding. An example early project would be to produce the complete network design for an upcoming cluster, oversee its installation, test the links and hand over as-built documentation to the operations team. • Experience designing networks for HPC or AI cluster deployments, with examples of designs you have owned from specification to installation., • A thorough understanding of the differences between out-of-band, frontend and accelerator (GPU) fabric networking., • Practical knowledge of InfiniBand, RoCE or similar lossless fabrics, including fat-tree and rail-optimised topologies., • A very clear understanding of data centre fibre optics: OS2 versus OM4/OM5, MTP/MPO versus LC, APC versus UPC, Type A/B/C polarity, pinned and unpinned connectors, and the losses introduced by patch panels, cassettes and connectors., • The ability to communicate a design clearly to installers, suppliers, project managers and engineers. Experience with 400G/800G Ethernet and OSFP/QSFP-DD optics, optical circuit switching or other novel interconnects, high-density liquid- or air-cooled racks, documentation tools such as NetBox, and colocation facilities would be useful. Depth in a subset is sufficient; the lab will keep presenting new requirements and we expect you to learn alongside us. • A collaborative and supportive work environment, • The opportunity to have a high impact in a growing organisation, • Competitive salary package, including pension and benefits, • Professional development opportunities, • Networking opportunities with influential figures from across the tech sector and academia, • A vibrant office environment located a few minutes' walk away from Cambridge train station This role is based in our Cambridge office with occasional travel to our lab in Kao Data, Harlow, particularly during builds and changes. Occasional travel to future sites elsewhere in the UK will be part of the role. Support is generally only required during UK business hours, although major installation work may occasionally be scheduled for evenings or weekends. We are open to permanent, part-time or consultancy arrangements and would like you to start as soon as practical. While our preference is for staff to be based in our Cambridge office we will consider hybrid and fully remote applicants. We particularly welcome experienced engineers but will also consider earlier-career candidates with strong evidence of ability. Qualifications and vendor certifications are useful but secondary to experience and the quality of your work. CommonAI CIC is an equal opportunity employer and is committed to creating an inclusive and diverse workplace. #J-18808-Ljbffr

CommonAI CIC is a non-profit organisation built around collaborative engineering and is backed by a mix of UK Government and private funding in order to design, build and deploy innovative AI systems. Our Scaling Inference Lab (SIL) is developing simulation grounded in real measurements to help the UK invest in promising AI infrastructure. We connect proposed hardware designs to realistic workloads, helping investors and engineers understand their potential, their limitations and the next experiment worth doing. Build simulation software that helps engineers and investors explore how AI systems would behave with different processors, memory systems, networks or serving strategies. Your work will help turn models and existing software into a practical framework for comparing design choices against realistic LLM workloads. You will develop and connect simulation components, making sure that changes in one part of a system have the appropriate consequences elsewhere. This means understanding unfamiliar code, designing clear interfaces and building software whose predictions can be investigated and tested against measurements. We aim to reuse and extend existing frameworks wherever they provide a sound foundation. We welcome depth in one or more relevant areas, including simulation, systems software, performance modelling and inference serving. Your initial focus will reflect your strengths and the components we choose to build on, with complementary expertise added as the team grows. • Develop and integrate simulation components that capture behaviour and interactions relevant to an AI infrastructure comparison., • Work with the technical lead and our partners to evaluate existing frameworks and software, extending and connecting useful components through clear interfaces., • Develop appropriate models of system state and resource use so that changes in one component have the appropriate consequences elsewhere., • Build reproducible simulation scenarios, diagnostic outputs and tests that expose incorrect assumptions, integration errors and unsupported cases., • Work with measurement and uncertainty colleagues to compare predictions with evidence, improve models and integrate calibration and uncertainty methods into the simulator., • Maintain reliable software, documentation and development workflows, using AI coding agents to support implementation and testing. An example early project would be to bridge an existing simulator component with our workload models and measurement datasets, producing a reproducible comparison between predicted and measured performance over two systems. • Strong software engineering skills and evidence of building, debugging and maintaining substantial technical software., • Experience in one or more of: discrete-event simulation; computer architecture or performance modelling; distributed/HPC systems; inference runtimes and serving; memory or network modelling; integration of scientific software and heterogeneous components., • The ability to understand unfamiliar code, reason about interfaces and assess the suitability and maintenance costs of reusing, extending or building software., • A collaborative approach to testing assumptions and working with specialists in adjacent areas., • Practical experience using AI coding agents, with the judgement to check their output for correctness and maintainability. Python, C++, Rust or other relevant systems or scientific programming experience can provide a route into the role. Familiarity with vLLM or SGLang, simulator frameworks, or hardware performance tools would be useful. Depth in a subset of these areas is sufficient; languages and frameworks will follow the engineering needs of the programme. • Help build UK AI capability and improve how investment and compute serve the public good., • Build a network spanning UK hardware SMEs, major technology companies, venture and public investors, and leading universities., • Shape a growing team with startup pace and close academic links., • Use frontier models and coding agents with a substantial tooling budget., • A collaborative and supportive work environment, • The opportunity to have a high impact in a growing organisation, • Competitive salary package, including pension and benefits, • Professional development opportunities, • Networking opportunities with influential figures from across the tech sector and academia, • A vibrant office environment located a few minutes’ walk away from Cambridge train station Hybrid, with Cambridge attendance agreed individually around collaboration and the work; applicants across the UK are welcome. We are open to permanent employment, part-time arrangements and consultancy. We would like you to start as soon as practical and can be flexible for the right person. We particularly welcome experienced engineers and can also consider earlier-career candidates with strong evidence of ability. Relevant postgraduate study is useful, but qualifications are secondary to experience and the quality of your work. #J-18808-Ljbffr

Main area Division A - Associate Operations Manager Grade Band 8b Contract 6 months (Secondment / Fixed Term) Hours • Full time, • Part time, • Flexible working37.5 hours per week (Full Time or Part Time / Flexible working hours may be considered.) Job ref 180-A-CVA1332 Employer Cambridge University Hospitals NHS Foundation Trust Employer type NHS Site Addenbrookes Hospital-Division A Town Cambridge Salary £66,582 - £77,368 p.a. pro rata Salary period Yearly Closing 15/11/2026 23:59 Interview date 24/11/2026 Are you an experienced operational leader looking for your next challenge? Do you have the vision, drive and leadership skills to make a real difference to the delivery of high-quality patient care? This is a fantastic opportunity for an ambitious leader to take on a key operational role, working with autonomy and authority to support the delivery of services, in the MSK directorate. You will play a pivotal role in the effective management and performance of your designated service areas, ensuring the delivery of key operational targets within available resources. You will work closely with clinical and operational colleagues, senior medical staff and divisional leaders to support service development, improve pathways and ensure services continue to meet the needs of our patients and the organisation. We are looking for a confident, proactive and resilient operational leader with the ability to manage complex services, influence stakeholders and deliver sustainable improvements in a demanding healthcare environment. The key focus of this post will be to deputise over the wholebusiness unit, working with autonomy and authority to act on behalf of the Operations Manager. Such duties would include restructuring of teams and changes to service or pathway delivery. Deputise for senior leaders within the Division in both planned and unplanned scenarios. The post holder will manage the service areas they are responsible for, ensuring the delivery of key targets within the budgeted resources available. They will contribute to strategic and annual business planning for the business unit, including the engagement of senior medical and other staff, as required. They will also lead the planning, development and implementation of service improvement, capital and other projects for the business unit for both internal and regional where appropriate. They will lead clinical governance processes within their business unit and assist in the management of risk. Cambridge University Hospitals (CUH) NHS Foundation Trust comprises Addenbrooke's Hospital and the Rosie Hospital in Cambridge. With over 13,000 staff and over 1100 beds the priorities of the Trust focus on a quality service which is all about people - patients, staff and partners. Recognised as providing 'outstanding' care to our patients and rated 'Good' overall by the Care Quality Commissioner, is testament to the skill and dedication of the people who work here. CUH's values - Together - Safe, Kind, Excellent - are at the heart of patient care, defining the way all staff work and behave. The Trust provides accessible high-quality healthcare for the local people of Cambridge, together with specialist services, dealing with rare or complex conditions for a regional, national and international population. CUH is committed to promoting a diverse and inclusive community - a place where we can all be ourselves. We value our differences and fully advocate and support an inclusive working environment where every individual can fulfil their potential. We want to ensure our people are truly representative of all the communities that we serve. We welcome applications for all positions in the organisation irrespective of people's age, disability, ethnicity, race, nationality, gender identity, sex, sexual orientation, religion or belief, marriage and civil partnership status, or pregnancy and maternity status or social economic background. Please see the attached Applicant Information Pack (combined Job Description and Person Specification) for key duties and responsibilities. Please note:- Internal applicants on permanent contracts can apply for this post as a secondment or fixed term contract. For secondments you must have the approval of your line manager before applying. If you are an internal applicant currently on a fixed term contract you are able to apply for this as a fixed term position. This vacancy will close at midnight on 15th November 2026. Interviews are due to be held on 24th November 2026. All appointments with a start date on or after 1 October 2026 will be subject to a 12-week probationary period. This provides a structured and supportive period to help you settle into your role, with regular reviews and feedback. Right to Work As there is a 12-week probationary period, successful candidates are expected to have the Right to Work for a period of at least 6 months from the proposed start date. Immigration and Right to Work status will be discussed with successful candidates at conditional offer. CUH will not offer sponsorship to candidates that have applied for a fee waiver. • We want to do all we can to support good working days., • We offer development opportunities and a wide range of benefits, including on-site leisure facilities, shopping concourse and day nurseries., • Our good work programme currently includes providing reduced cost Stagecoach bus travel to and from Cambridge University Hospital site., • Park and Ride bus journeys between Babraham Road and Trumpington sites are free, as is the route to and from Cambridge train station and our hospitals., • We also subsidise the cost of parking on site for eligible staff., • On CUH campus, hot food is available 24/7 and at a reduced cost for colleagues., • Recently we launched the first of our staff pod break spaces. Located in the Deakin Centre, we have a purpose-created colleague-only café, with free tea and coffee, a break space and private outside area for colleagues to rest, refuel and recharge., • CUH is committed to assisting employees in achieving a good work-life balance irrespective of role or personal circumstances. Flexible arrangements may include, but are not limited to, part-time working, job-share, term-time working and flexible start and finish times., • Please note if you would like to discuss the required hours of this role further, you should approach the contact given. In some cases, alternative working hours will be considered. We welcome applications from the Armed Forces. • Educated to First Degree Level or equivalent (A), • Educated to masters level or equivalent level of experience of working at a senior level in specialist areas (A), • Evidence of CPD (A), • Completion of or willingness to work towards CUH Improvement Coach status (I), • Project Management or Improvement and Transformation Qualification (Prince II, Six Sigma or Lean) (A), • Proven experience in senior operational management within a clinical area covering multiple specialities and pathways. Knowledge of inpatient, outpatient and diagnostic requirements of pathways.(A), • Proven experience in improvement and service transformation(A/I), • Experience in the development and use of a range of IT systems(A/I), • Proven experience of managing a team including; o Appraisal skills o Recruitment and selection skills o Disciplinary and capability procedures o Experience in workforce planning to ensure service continuity, • Evidence of continuing professional development(A/I), • Experience in finance, policy and service development changes(A/I), • Substantial proven experience of budget management on a large scale (including activity planning), • Proven experience of establishing and leading on national strategic work in relation to policy development, operations and systems(A/I), • Experience in delivering complex healthcare-related improvement projects and programmes and working alongside senior clinical and operational professionals(A/I), • Extensive knowledge of Operational strategy and policy within the Trust, region and NHS. Including an understanding of tertiary treatment pathways, and the commissioning of new pathways and services. (A/I), • Knowledge of audit and governance frameworks(A/I), • Knowledge and understanding of best practice risk management processes(A/I), • Must have an comprehensive understanding of the background to and aims of current healthcare policy and appreciate the implications of this on engagement (A/I), • Comprehensive understanding of the relationship between the Department of Health, NHS England and individual provider and commissioning organisations (A/I), • Extensive knowledge of business and activity planning processes(A/I), • Extensive knowledge of risk management processes(A/I), • Knowledge of IT/technology used in a clinical environment, • Overview knowledge and understanding of the NHS, including funding and organisational realities(A/I), • Knowledge of research and development (A/I), • Proven leadership skills at a senior management level (A/I), • Excellent and effective verbal and written communication at a senior level(A/I), • Proven written skills including business cases and Divisional Board Reports. (A/I), • Proven ability to work in an organised manner and to high standards(A/I), • Excellent presentation skills for conveying complex concepts to a range of audiences(A/I), • Excellent attention to detail and the ability to meet multiple and competing deadlines(A/I), • Problem solving/trouble shooting skills(A/I), • Excellent skills in the use of Microsoft office applications (including Excel, Word and PowerPoint) and databases(A/I), • Highly developed communication and negotiation skills for delivering key and contentious messages to a range of stakeholders both internal and external (including outside the NHS) to the organisation, some at very senior level(A/I), • Ability to use informed persuasion to influence others(A/I), • Ability to identify risks, anticipate issues and create solutions and to resolve problems in relation to or service delivery(A/I), • Ability to understand a broad range of complex information quickly and making decisions where opinions differ/no obvious solution (A/I), • Evidence of planning and delivering programmes and projects and services, on time (A/I), • Financial and staff management (A/I), • Ability to work without supervision, providing specialist advice to the organisation, working to tight and often changing timescales (A/I), • Ability to deputise for senior leaders within the Division in both planned and unplanned scenarios (A/I), • Ability to consider the most effective way to promote equality of opportunity and good working relationships in employment and service delivery and to take actions which support and promote this agenda. (A/I), • Ability to build trusted stakeholder relationships and wide support networks in a political context(A/I), • Ability to lead people with vision and integrity, demonstrating the courage to do the right thing. (A/I), • Ability to motivate high performing teams, mobilising individuals and team goals(A/I), • Ability to build effective partnerships, common purpose and shared understanding across a range of diverse and competing views (A/I), • Competent at working with quantative and qualitative data to deliver relevant, robust and action-oriented conclusions(A/I), • Prepared to be flexible in relation to working hours (A/I), • Team working skills (A/I), • Self-motivated (A/I), • Ability to move between details and the bigger picture(A/I), • Demonstrates honesty and integrity and promotes organisational values (A/I), • Embrace change, viewing it as an opportunity to learn and develop (A/I), • Operate with a high degree of autonomy, able to exercise judgement and challenge both externally and internally to drive services forward and achieve its agreed outcomes, with minimal steer and input from senior staff(A/I), • Be analytical and objective, ensuring that decisions are evidence-based, and aligned to the key principles of the transformation process. (A/I), • Always have the best interest of patients and staff at the centre of any plans(A/I), • The ability to understand and behave at all times, towards patients, visitors and colleagues according to the Trust values of safe, kind, excellent. (A/I) Name Federico Santoro Job title Associate Director of Operations (Acting) Email address Telephone number Additional information Gill Love Operations Manager - MSK Division A Cambridge University Hospitals NHS Foundation Trust Ext 388178 | #J-18808-Ljbffr

This is an exciting opportunity to join our growing Cambridge office as a Senior Associate within our Cost Management (Quantity Surveying) team. We are looking for a hands-on, delivery-focused individual who enjoys taking ownership of projects while supporting the development of this key service. You will play an important role within the team, combining strong technical delivery with increasing client responsibility. Our Cambridge office is based in a modern space close to the train station, offering excellent connectivity and a high-quality working environment. The region continues to see strong investment across education, life sciences, infrastructure, defence, residential and energy, creating a varied and exciting pipeline of work. This is a role for someone who wants to take the next step in their career — developing leadership skills, building client relationships and positioning themselves for future progression. • Delivering Cost Management / Quantity Surveying services across a range of projects, • Taking ownership of project delivery from early stage through to completion, • Supporting pre- and post-contract activities including cost planning, procurement and reporting, • Administering contracts (including NEC and JCT) and managing commercial processes, • Building strong relationships with clients, consultants and contractors, • Supporting and mentoring junior team members, • Contributing to the overall performance and growth of the Cambridge team What you need to do to be effective in this role You will be a reliable, proactive individual who enjoys delivering high-quality work and taking ownership of your projects. You are developing confidence in client interactions and are keen to play a broader role within the team. You bring a positive, “can-do” mindset, with the ambition to grow and take on more responsibility over time. • Experience in Cost Management / Quantity Surveying within a consultancy, • Strong technical capability across pre- and post-contract delivery, • Experience working with NEC and JCT contracts, • Ability to manage projects with increasing independence, • Exposure to mentoring or supporting junior team members, • Degree qualified in a relevant discipline, • MRICS (or working towards) What’s in it for you? • A clear pathway toward Associate Partner level and beyond, • Opportunity to work on diverse, high-quality projects, • A supportive and collaborative team environment, • A modern, well-connected Cambridge office, • Competitive salary and benefits package, • Flexible and hybrid working, • Ongoing professional development and APC support #J-18808-Ljbffr

Design, build and keep evolving the physical networks that connect the clusters in the Scaling Inference Lab (SIL). The SIL, backed by ARIA, is a neutral, UK-based testbed where new AI technologies (accelerators, memory systems and interconnects) are tested at rack scale against real workloads for the very first time. The results help UK startups, investors and public compute programmes decide what to build, fund and buy, and they build lasting UK capability in the infrastructure that AI depends on. The network is critical to every experiment and this role will own the cabling, switch layouts and topologies for the out-of-band, frontend and accelerator fabrics in our lab at Kao Data in Harlow, and in any further facilities we establish in the UK. The SIL is an R&D facility and its requirements evolve rapidly. A new cluster might need a rail-optimised InfiniBand fabric, an 800G Ethernet frontend or an optical circuit-switched interconnect that has never been deployed at this scale. An older cluster might need re-cabling so its equipment can be reused in the next one. You will gather requirements from members, CommonAI staff and other key stakeholders to create flexible and resilient network designs. In this role you will work closely with both technical staff and the project management team to provide the detailed designs that suppliers, cabling contractors and installers need. • Design the physical network for each new cluster across out-of-band, frontend/storage and accelerator fabrics: topology, switch placement, port maps, cable schedules and bills of materials, • Specify fibre and copper infrastructure in detail, including fibre grade, connectors, polarity, pinning, trunk and cassette layouts, patch-panel arrangements and end-to-end loss budgets, and select matching transceivers, DACs and AOCs, • Plan changes to existing clusters so equipment can be re-cabled and reused with minimal disruption to running work, • Act as the technical authority for suppliers and installers: produce drawings and specifications, review quotations, and inspect and test completed work, • Maintain accurate, well-labelled as-built documentation of every rack, cable, port and link, • Work with project managers on sequencing and lead times, and with operators and software colleagues on bring-up and fault-finding An example early project would be to produce the complete network design for an upcoming cluster, oversee its installation, test the links and hand over as-built documentation to the operations team. • Experience designing networks for HPC or AI cluster deployments, with examples of designs you have owned from specification to installation, • A thorough understanding of the differences between out-of-band, frontend and accelerator (GPU) fabric networking, • Practical knowledge of InfiniBand, RoCE or similar lossless fabrics, including fat-tree and rail-optimised topologies, • A very clear understanding of data centre fibre optics: OS2 versus OM4/OM5, MTP/MPO versus LC, APC versus UPC, Type A/B/C polarity, pinned and unpinned connectors, and the losses introduced by patch panels, cassettes and connectors, • The ability to communicate a design clearly to installers, suppliers, project managers and engineers Experience with 400G/800G Ethernet and OSFP/QSFP-DD optics, optical circuit switching or other novel interconnects, high-density liquid- or air-cooled racks, documentation tools such as NetBox, and colocation facilities would be useful. Depth in a subset is sufficient; the lab will keep presenting new requirements and we expect you to learn alongside us. • A collaborative and supportive work environment, • The opportunity to have a high impact in a growing organisation, • Competitive salary package, including pension and benefits, • Professional development opportunities, • Networking opportunities with influential figures from across the tech sector and academia, • A vibrant office environment located a few minutes' walk away from Cambridge train station This role is based in our Cambridge office with occasional travel to our lab in Kao Data, Harlow, particularly during builds and changes. Occasional travel to future sites elsewhere in the UK will be part of the role. Support is generally only required during UK business hours, although major installation work may occasionally be scheduled for evenings or weekends. We are open to permanent, part-time or consultancy arrangements and would like you to start as soon as practical. While our preference is for staff to be based in our Cambridge office we will consider hybrid and fully remote applicants. We particularly welcome experienced engineers but will also consider earlier-career candidates with strong evidence of ability. Qualifications and vendor certifications are useful but secondary to experience and the quality of your work. CommonAI CIC is an equal opportunity employer and is committed to creating an inclusive and diverse workplace. #J-18808-Ljbffr

CommonAI CIC is a non-profit membership organisation, founded on a belief in collaborative engineering for the safe and responsible development of foundational AI technologies. A place where AI startups, enterprises large and small, public sector bodies and academia can share resources and knowledge, to codevelop and grow businesses, fast. We are led by experienced founders, investors and engineers who believe that collaborative engineering drives faster AI innovation and are backed by a mix of UK Government and private funding in order to design, build and deploy innovative AI systems. The Opportunity We are seeking a GPU Kernel and Performance Engineer to make our research models run fast on real hardware, primarily NVIDIA GPUs. Our research team works on reducing the complexity of large language models while retaining their performance. Some of this work informs our analyses of the trade-offs between training and inference costs when scaling inference workloads. The work runs on the UK's national AI supercomputers and on our own compute nodes. You will join a small research team and turn new model designs into efficient GPU code, then measure how much time and memory traffic they cost on real hardware. Our training runs on NVIDIA GPUs, and we also expect to work with other AI accelerators, so there is scope to learn new chips and programming models as the work develops. We care more about how well you understand hardware and memory than about ML research credentials. What You'll Do • Write and optimise CUDA and Triton kernels on NVIDIA GPUs for sparse and structured model components, including block-sparse matrix operations and attention variants., • Adapt and extend open-source kernels, such as FlashAttention-style and block-sparse attention, to new model designs., • Profile training and inference to find where time and memory traffic go, using tools such as NVIDIA Nsight Systems and Nsight Compute., • Optimise for the GH200's memory hierarchy on Isambard-AI and for our own GPU nodes., • Build tools that measure data movement per generated token, and check analytic cost models against hardware measurements., • Test every kernel against reference implementations before it runs at scale., • Work with our training infrastructure engineer to raise training throughput, and with researchers to make new designs fast enough to test., • Evaluate how our models map onto other AI accelerators, such as TPUs and dataflow, wafer-scale and compute-in-memory architectures, and port key components to them where it is useful., • Contribute improvements back to open-source projects where possible., • Proven experience writing and optimising GPU kernels in CUDA or Triton, ideally on recent NVIDIA architectures., • A strong understanding of GPU architecture and memory hierarchies (e.g. shared memory, HBM bandwidth, memory coalescing, occupancy)., • Hands-on experience profiling and optimising performance with tools such as NVIDIA Nsight Compute or Nsight Systems., • Strong Python and C/C++., • A rigorous approach to testing numerical code (e.g. comparison against reference implementations, mixed-precision error)., • Openness to learning new hardware architectures and programming models, often from limited documentation., • A strong enthusiasm for using LLMs, coding assistants, and agents as core tools in your own software development process. We also value: • Experience with attention kernels (e.g. FlashAttention) or sparse kernels (e.g. block sparsity, mixture-of-experts routing)., • Experience with NVIDIA Hopper features (e.g. TMA, warpgroup matrix instructions) or libraries such as CUTLASS and CuTe., • Experience with multi-GPU communication (e.g. NCCL, NVLink)., • An understanding of LLM inference performance (e.g. KV caching, memory-bound decoding, quantisation)., • Experience with other AI accelerators (e.g. AMD GPUs, TPUs with JAX and Pallas, or dataflow, wafer-scale and compute-in-memory architectures)., • Experience with compiler stacks for AI hardware (e.g. MLIR, XLA, TVM)., • A background in computer architecture, HPC, graphics or game engine programming., • Experience with hardware/software co-design, or with hardware performance modelling and simulation., • Contributions to performance-critical open-source projects (e.g. PyTorch, Triton, JAX, vLLM)., • A collaborative and supportive work environment, • The opportunity to have a high impact in a growing organisation, • Competitive salary package and pension, • Professional development opportunities, • Networking opportunities with influential people from across the tech sector, financial services, and academia, • A vibrant office environment located a few minutes' walk away from Cambridge train station CommonAI CIC is an equal opportunity employer and is committed to creating an inclusive and diverse workplace. #J-18808-Ljbffr

CommonAI CIC is redefining deep-tech AI solutions through collaborative engineering and shared infrastructure. Working at the intersection of AI, cloud and large-scale infrastructure, we partner with commercial, government and research entities to accelerate AI adoption across the UK and Europe. Backed by UK Government and private funding, our team of experienced founders and engineers is building the future of AI systems. Its members include ARIA, Barclays and a fast-growing group of deeptech startups. Highly regulated industries like financial services need guaranteed, predictable outcomes that today's large language models cannot provide on their own. Our High Assurance programme will develop AI systems which can be used safely in environments where accuracy and explainability are critical building blocks in creating a required level of trust. • Formal methods and verification: proofs that an AI system must satisfy before it can act., • Domain-specific small language models for regulated use cases., • AI control platforms that govern how AI systems behave in production. The programme launched recently, with Barclays as its lead member and held its first workshop, "Towards Trustworthy Harnesses", with members, academics and domain experts. We are looking for a Programme Technical Lead to drive execution of the High Assurance AI Programme. The programme already has an academic architectural lead; this role turns the programme's research direction into delivered engineering, working closely with the architectural lead, our Chief Science Officer and our members. You will report to the CTO and build and lead the programme's engineering team. • Own delivery of the programme's technical roadmap, from research direction to working infrastructure., • Build, hire and lead an engineering team based in Cambridge., • Translate the needs of regulated industries into clear technical requirements, making sure the programme solves the problems which matter to members., • Bring members, researchers and partners together around a shared roadmap., • Work with the architectural lead to evaluate approaches such as formal methods, AI control and small language models and decide what to build., • Represent the programme technically with members and partners. We do not expect anyone to have all of the experience below. Deep expertise in one area, combined with the ability to work across the others, matters more than ticking every box. • Understanding/experience of processes/workflows (and their associated constraints) in the financial services industry, particularly banks., • Understanding of how AI is or might be used, governed or assured in a regulated industry, ideally financial services (for example model risk, AI governance or controls)., • Experience with PyTorch or other similar frameworks., • Enough technical depth to engage credibly with ML researchers and understand the problems the programme needs to solve, without needing to be the person who writes the proofs or algorithms., • Ability to bring together organisations with different priorities and build a shared plan., • Experience leading an engineering team and driving delivery in a technical, research-driven environment., • Experience with formal methods, verification, AI safety, AI assurance or AI control., • Experience with small or domain-specific language models., • Relationships with banks, insurers or regulators., • Experience in a startup, or building a new product, or independent project / research that has resulted in software used by others. Lead a new programme at the boundary of research and industry, helping to solve tough challenges such as how AI can be used safely in critical industries. High-impact role in a rapidly growing, well-funded organisation. Mission-led work for the public good, released over time as managed open source. • Competitive salary, pension, healthcare and benefits, including company share scheme., • Networking opportunities with leaders across tech and academia., • Vibrant office situated near to Cambridge train station. CommonAI CIC is an equal opportunity employer and is committed to creating an inclusive and diverse workplace. #J-18808-Ljbffr

CommonAI CIC is a non-profit organisation built around collaborative engineering and is backed by a mix of UK Government and private funding in order to design, build and deploy innovative AI systems. Our Scaling Inference Lab (SIL) is developing simulation grounded in real measurements to help the UK invest in promising AI infrastructure. We connect proposed hardware designs to realistic workloads, helping investors and engineers understand their potential, their limitations and the next experiment worth doing. Build the workloads and benchmarks that reveal how AI infrastructure performs on useful applications. You will develop reproducible LLM serving workloads, connecting application behaviour and requirements to physical measurements and SIL's simulation platform. The initial focus includes long-context inference and agentic workloads. You will investigate how request patterns, context lengths, caching, repeated model calls and interactions with tools shape the demands placed on a system. Your benchmarks will make performance comparisons meaningful by specifying the work being done and the quality and service requirements it must meet. Working with measurement and simulation engineers and our partners, you will help preserve the workload behaviour that matters when moving between real systems and models. Your work will help engineers and investors understand which infrastructure choices suit particular applications, and how conclusions change when the workload changes. • Build and maintain reproducible LLM serving workloads, serving configurations and benchmark tools, including long-context and agentic workloads, using suitable existing software., • Characterise relevant behaviour such as request arrivals, prompt and output lengths, caching, batching, tool interactions and dependencies between requests., • Define performance measures alongside application-quality and service requirements, so comparisons reflect useful work under stated conditions., • Work with measurement engineers to reproduce workloads on physical systems, and with simulator engineers to develop workload representations that preserve the behaviour and dependencies that matter., • Investigate how changes in workloads and serving configurations affect conclusions, and keep the benchmark suite relevant as inference technology and workloads evolve., • Document datasets, model and software versions, configurations, provenance and permitted use so other engineers can reproduce and extend the work., • An example early project would be to build a reproducible agentic workload and work with measurement colleagues to compare its performance across two systems, under agreed application-quality and service requirements., • Experience building or evaluating ML systems, inference services, performance benchmarks or comparable workload-driven systems., • Strong practical software skills for constructing reliable benchmark tools, analysing results and debugging interactions between applications and infrastructure., • Understanding of LLM inference and an interest in how serving choices affect performance and output quality., • Care in designing reproducible comparisons and interpreting results against stated application requirements., • Practical experience using AI coding agents, with the judgement to validate generated code, data transformations and evaluations., • Experience with vLLM, SGLang or similar serving software, long-context workloads, agentic applications, trace analysis or quality evaluation would be valuable. Depth in a subset is sufficient. The initial remit is LLM serving; other forms of inference are possible later extensions., • Help build UK AI capability and improve how investment and compute serve the public good., • Build a network spanning UK hardware SMEs, major technology companies, venture and public investors, and leading universities., • Shape a growing team with startup pace and close academic links., • Use frontier models and coding agents with a substantial tooling budget., • A collaborative and supportive work environment, • The opportunity to have a high impact in a growing organisation, • Competitive salary package, including pension and benefits, • Professional development opportunities, • Networking opportunities with influential figures from across the tech sector and academia, • A vibrant office environment located a few minutes’ walk away from Cambridge train station Hybrid, with Cambridge attendance agreed individually around collaboration and the work; applicants across the UK are welcome. We are open to permanent employment, part-time arrangements and consultancy. We would like you to start as soon as practical and can be flexible for the right person. We particularly welcome experienced engineers and can also consider earlier-career candidates with strong evidence of ability. Relevant postgraduate study is useful, but qualifications are secondary to experience and the quality of your work. #J-18808-Ljbffr

CommonAI CIC is a non-profit organisation built around collaborative engineering and is backed by a mix of UK Government and private funding in order to design, build and deploy innovative AI systems. Our Scaling Inference Lab (SIL) is developing simulation grounded in real measurements to help the UK invest in promising AI infrastructure. We connect proposed hardware designs to realistic workloads, helping investors and engineers understand their potential, their limitations and the next experiment worth doing. Build the software that lets SIL configure and control real AI infrastructure for reproducible experiments. You will turn the capabilities of our experimental clusters into programmatic controls for changes such as workload placement, resource allocation and inference-serving configuration. The engineering challenge is making those changes dependable: understanding the current state, checking whether an action can be carried out, confirming what actually happened and handling failures. Your software will make it possible to repeat experiments and understand which configuration produced a result. Working with measurement and simulation engineers and cluster operators, you will establish useful controls for the first experimental studies. Those interfaces will also provide the actions that future design-space optimisation software can call. • Identify the controls available on the selected clusters and expose them through documented, programmatic interfaces with clear supported actions, • Automate configuration and experiment setup across relevant parts of the stack, such as workload placement, serving settings, resource allocation or device configuration, • Build reliable execution with precondition checks, confirmation of the resulting state, error handling and recovery to a known configuration where supported, • Record requested actions, outcomes and resulting configuration so experiments can be reproduced and measurements interpreted correctly, • Coordinate access, permissions and changes with cluster operators, and test success, failure and recovery paths within an agreed experimental scope, • Design interfaces that future optimisation software can call, with clear capability limits and explicit outcomes when an action cannot be completed An example early project would be to build an interface for changing serving configurations and demonstrate a repeatable experiment across two configurations, including checks of the applied settings and recovery from a failed change. • Experience building systems software or automation that configures and controls real computing infrastructure, • Strong programming and debugging skills, with practical understanding of Linux and the relevant hardware/software interfaces, • Depth in one or more of: cluster or HPC automation, distributed systems, orchestration, device control, networking, or experiment-control software, • The ability to reason about partial failure, state, permissions and the consequences of changing a running system, • Practical experience using AI coding agents, with the judgement to validate generated code and test changes that affect hardware or infrastructure Experience with accelerators, job-control systems, Kubernetes, Slurm, operating systems (NixOS, CoreOS) or IaC tools (Terraform/OpenTofu) would be useful. Depth in a subset is sufficient; the initial controls and integration choices will follow the needs and capabilities of SIL's clusters. • Help build UK AI capability and improve how investment and compute serve the public good, • Build a network spanning UK hardware SMEs, major technology companies, venture and public investors, and leading universities, • Shape a growing team with startup pace and close academic links, • Use frontier models and coding agents with a substantial tooling budget, • A collaborative and supportive work environment, • The opportunity to have a high impact in a growing organisation, • Competitive salary package, including pension and benefits, • Professional development opportunities, • Networking opportunities with influential figures from across the tech sector and academia, • A vibrant office environment located a few minutes' walk away from Cambridge train station Hybrid, with regular time in Cambridge agreed individually around hardware work and collaboration; applicants across the UK are welcome. We are open to permanent employment, part-time arrangements and consultancy. We would like you to start as soon as practical and can be flexible for the right person. We particularly welcome experienced engineers but will also consider earlier-career candidates with strong evidence of ability. Relevant postgraduate study is useful, but qualifications are secondary to experience and the quality of your work. CommonAI CIC is an equal opportunity employer and is committed to creating an inclusive and diverse workplace. #J-18808-Ljbffr

CommonAI CIC is a non‑profit organisation built around collaborative engineering and is backed by a mix of UK Government and private funding in order to design, build and deploy innovative AI systems. Our Scaling Inference Lab (SIL) is developing simulation grounded in real measurements to help the UK invest in promising AI infrastructure. We connect proposed hardware designs to realistic workloads, helping investors and engineers understand their potential, their limitations and the next experiment worth doing. Build simulation software that helps engineers and investors explore how AI systems would behave with different processors, memory systems, networks or serving strategies. Your work will help turn models and existing software into a practical framework for comparing design choices against realistic LLM workloads. You will develop and connect simulation components, making sure that changes in one part of a system have the appropriate consequences elsewhere. This means understanding unfamiliar code, designing clear interfaces and building software whose predictions can be investigated and tested against measurements. We aim to reuse and extend existing frameworks wherever they provide a sound foundation. We welcome depth in one or more relevant areas, including simulation, systems software, performance modelling and inference serving. Your initial focus will reflect your strengths and the components we choose to build on, with complementary expertise added as the team grows. • Develop and integrate simulation components that capture behaviour and interactions relevant to an AI infrastructure comparison., • Work with the technical lead and our partners to evaluate existing frameworks and software, extending and connecting useful components through clear interfaces., • Develop appropriate models of system state and resource use so that changes in one component have the appropriate consequences elsewhere., • Build reproducible simulation scenarios, diagnostic outputs and tests that expose incorrect assumptions, integration errors and unsupported cases., • Work with measurement and uncertainty colleagues to compare predictions with evidence, improve models and integrate calibration and uncertainty methods into the simulator., • Maintain reliable software, documentation and development workflows, using AI coding agents to support implementation and testing., • An example early project would be to bridge an existing simulator component with our workload models and measurement datasets, producing a reproducible comparison between predicted and measured performance over two systems., • Strong software engineering skills and evidence of building, debugging and maintaining substantial technical software., • Experience in one or more of: discrete‑event simulation; computer architecture or performance modelling; distributed/HPC systems; inference runtimes and serving; memory or network modelling; integration of scientific software and heterogeneous components., • The ability to understand unfamiliar code, reason about interfaces and assess the suitability and maintenance costs of reusing, extending or building software., • A collaborative approach to testing assumptions and working with specialists in adjacent areas., • Practical experience using AI coding agents, with the judgement to check their output for correctness and maintainability. Python, C++, Rust or other relevant systems or scientific programming experience can provide a route into the role. Familiarity with vLLM or SGLang, simulator frameworks, or hardware performance tools would be useful. Depth in a subset of these areas is sufficient; languages and frameworks will follow the engineering needs of the programme. • Help build UK AI capability and improve how investment and compute serve the public good., • Build a network spanning UK hardware SMEs, major technology companies, venture and public investors, and leading universities., • Shape a growing team with startup pace and close academic links., • Use frontier models and coding agents with a substantial tooling budget., • A collaborative and supportive work environment, • The opportunity to have a high impact in a growing organisation, • Competitive salary package, including pension and benefits, • Professional development opportunities, • Networking opportunities with influential figures from across the tech sector and academia, • A vibrant office environment located a few minutes’ walk away from Cambridge train station Hybrid, with Cambridge attendance agreed individually around collaboration and the work; applicants across the UK are welcome. We are open to permanent employment, part‑time arrangements and consultancy. We would like you to start as soon as practical and can be flexible for the right person. We particular welcome experienced engineers and can also consider earlier‑career with strong evidence of ability. Relevant postgraduate study is useful, but qualifications are secondary…?? CommonAI CIC is an equal opportunity employer and is committed to creating an inclusive and diverse workplace. #J-18808-Ljbffr

CommonAI CIC is a non-profit organisation built around collaborative engineering and is backed by a mix of UK Government and private funding in order to design, build and deploy innovative AI systems. Our Scaling Inference Lab (SIL) is developing simulation grounded in real measurements to help the UK invest in promising AI infrastructure. We connect proposed hardware designs to realistic workloads, helping investors and engineers understand their potential, their limitations and the next experiment worth doing. Lead the engineering team building a simulation platform for AI inference systems. You will bring together realistic workloads, hardware models and physical measurements to predict system performance and make the uncertainty in those predictions explicit. The platform will help engineers and investors assess emerging AI infrastructure, understand design choices, and identify next experiments. You will turn scientific direction into an implementable architecture and delivery plan, help shape recruitment, and develop a team with complementary expertise across software, modelling, measurement and hardware. You will report to the SIL Scientific Director and line manage a group of engineers in the growing SIL team. Working closely with the Director of Engineering, you will provide daily technical leadership and help shape the team as we recruit. Technical judgement and the ability to bring people with you matter more than having held a particular management title in the past. • Own the simulator codebase, working closely with the Scientific Director, SIL on architectural decisions. Translate the programme's scientific goals into an achievable engineering roadmap., • Assess existing simulators, serving software and partner components; decide what to reuse, extend or build, and establish interfaces that make the pieces work together., • Shape recruitment, assess candidates and mentor colleagues and line-manage engineers, building complementary expertise across software, measurement, modelling and hardware., • Ensure simulator predictions are tested against physical measurements and realistic LLM workloads, with uncertainty made explicit to the people interpreting the results., • Establish engineering practices for maintainable software, testing, reproducible experiments and effective use of AI coding agents; review designs and results with the relevant specialists., • Contribute directly to implementation and debugging when engineering coverage is incomplete or a colleague needs support. An example early project would be to lead a complete study from an investor's question through simulation and measured validation to a useful evidence report, while establishing the team and foundations for subsequent cases. • Substantial industrial engineering experience delivering complex software or hardware/software systems, with clear examples of technical decisions you have owned., • Depth in at least one of systems simulation, computer architecture, distributed or HPC systems, inference serving, or experimental performance engineering. Experience across more than one is especially valuable., • Evidence that you can lead people through difficult technical work, explain trade-offs and develop others. This may come from technical leadership and mentoring as well as formal management., • The judgement to turn high-level scientific direction into independent engineering decisions, and to recognise when wider input is needed., • Practical engineering ability to review code and designs, investigate failures and help colleagues resolve implementation difficulties., • Experience using AI coding agents, with the ability to assess their output and help colleagues use them effectively. Experience connecting research prototypes to maintained engineering systems, collaborating with universities or technology suppliers, and comparing models with measurements would be valuable. We do not expect you to be the deepest specialist in every part of the programme. • Help build UK AI capability and improve how investment and compute serve the public good., • Build a network spanning UK hardware SMEs, major technology companies, venture and public investors, and leading universities., • Shape a growing team with startup pace and close academic links., • Use frontier models and coding agents with a substantial tooling budget., • A collaborative and supportive work environment, • The opportunity to have a high impact in a growing organisation, • Competitive salary package, including pension and benefits, • Professional development opportunities, • Networking opportunities with influential figures from across the tech sector and academia, • A vibrant office environment located a few minutes' walk away from Cambridge train station Hybrid working, with Cambridge attendance agreed individually around collaboration and job need. We would like you to start as soon as practical and can be flexible for the right person. We value engineering quality and demonstrated leadership over a particular qualification or previous management title. CommonAI CIC is an equal opportunity employer and is committed to creating an inclusive and diverse workplace. #J-18808-Ljbffr

CommonAI CIC is a non‑profit organisation built around collaborative engineering and is backed by a mix of UK Government and private funding in order to design, build and deploy innovative AI systems. Our Scaling Inference Lab (SIL) is developing simulation grounded in real measurements to help the UK invest in promising AI infrastructure. We connect proposed hardware designs to realistic workloads, helping investors and engineers understand their potential, their limitations and the next experiment worth doing. Build the software that lets SIL configure and control real AI infrastructure for reproducible experiments. You will turn the capabilities of our experimental clusters into programmatic controls for changes such as workload placement, resource allocation and inference‑serving configuration. The engineering challenge is making those changes dependable: understanding the current state, checking whether an action can be carried out, confirming what actually happened and handling failures. Your software will make it possible to repeat experiments and understand which configuration produced a result. Working with measurement and simulation engineers and cluster operators, you will establish useful controls for the first experimental studies. Those interfaces will also provide the actions that future design‑space optimisation software can call. • Identify the controls available on the selected clusters and expose them through documented, programmatic interfaces with clear supported actions., • Automate configuration and experiment setup across relevant parts of the stack, such as workload placement, serving settings, resource allocation or device configuration., • Build reliable execution with precondition checks, confirmation of the resulting state, error handling and recovery to a known configuration where supported., • Record requested actions, outcomes and resulting configuration so experiments can be reproduced and measurements interpreted correctly., • Coordinate access, permissions and changes with cluster operators, and test success, failure and recovery paths within an agreed experimental scope., • Design interfaces that future optimisation software can call, with clear capability limits and explicit outcomes when an action cannot be completed., • An example early project would be to build an interface for changing serving configurations and demonstrate a repeatable experiment across two configurations, including checks of the applied settings and recovery from a failed change., • Experience building systems software or automation that configures and controls real computing infrastructure., • Strong programming and debugging skills, with practical understanding of Linux and the relevant hardware/software interfaces., • Depth in one or more of: cluster or HPC automation, distributed systems, orchestration, device control, networking, or experiment‑control software., • The ability to reason about partial failure, state, permissions and the consequences of changing a running system., • Practical experience using AI coding agents, with the judgement to validate generated code and test changes that affect hardware or infrastructure., • Experience with accelerators, job‑control systems, Kubernetes, Slurm, operating systems (NixOS, CoreOS) or IaC tools (Terraform/OpenTofu) would be useful. Depth in a subset is sufficient; the initial controls and integration choices will follow the needs and capabilities of SIL's clusters., • Help build UK AI capability and improve how investment and compute serve the public good., • Build a network spanning UK hardware SMEs, major technology companies, venture and public investors, and leading universities., • Shape a growing team with startup pace and close academic links., • Use frontier models and coding agents with a substantial tooling budget., • A collaborative and supportive work environment, • The opportunity to have a high impact in a growing organisation, • Competitive salary package, including pension and benefits, • Professional development opportunities, • Networking opportunities with influential figures from across the tech sector and academia, • A vibrant office environment located a few minutes’ walk away from Cambridge train station Hybrid, with regular time in Cambridge agreed individually around hardware work and collaboration; applicants across the UK are welcome. We are open to permanent employment, part‑time arrangements and consultancy. We would like you to start as soon as practical and can be flexible for the right person. We particularly welcome experienced engineers but will also consider earlier‑career candidates with strong evidence of ability. Relevant postgraduate study is useful, but qualifications are secondary to experience and the quality of your work. #J-18808-Ljbffr

At lowRISC we believe that open source development can transform the semiconductor industry. To help achieve this vision of production-ready, commercial-grade chips and silicon building blocks, we employ a software team with a wide range of skills. Our software engineers work across the full software stack, from firmware and toolchains that interact directly with hardware to cloud-based infrastructure that tests our products at scale. This includes work in areas such as: • RISC-V firmware and driver development, • Software hardening and security tools, • Embedded operating systems (in Rust and C), • Compilers, code generators, and linters, • Simulators and emulators (e.g. QEMU), • Electronic design automation (EDA) tools, • Performance and code size analysis, • Cloud-based infrastructure (such as continuous integration pipelines) lowRISC is a non-profit Community Interest Company. With a business model that includes member fees and engineering services, we have shown that it is possible to run world-leading projects collaboratively for everyone’s benefit. Originally a spinout from Cambridge University’s Computer Lab, our UK team is based in Cambridge and our Switzerland team is based in Zurich. Our Ibex CPU and OpenTitan Root of Trust projects have been taken to production silicon, partnering with world-leading organisations including Google, combining open-source approaches with best-practice chip design methodologies. • Highly proficient in Rust programming; deep understanding of fundamental concepts such as the ownership model, lifetimes, concurrency, parallelism, memory management and procedural macros, • Solid knowledge of the Rust ecosystem, including tools and libraries, • A high standard of written and spoken English, • Experience designing, implementing, and maintaining large-scale or modular Rust codebases, • Expert understanding of unsafe Rust, FFI, and low-level systems programming, • Strong proficiency in C; familiarity with C++ and Python are a plus, • Solid understanding of compiler internals; experience with LLVM is a plus, • Experience with build systems and reproducible tooling (e.g. Bazel, Nix, uv), • Experience developing for embedded or resource-constrained environments, including real-time systems, • Familiarity with CI/CD for embedded or systems software, • Strong knowledge of computer architecture; some experience with assembly languages (x86, RISC-V); familiarity with hardware design is a plus, • Good knowledge of Linux, • Experience contributing to open source projects, • Higher degree levels and academic research experience are highly valued Salary dependent upon experience. Benefits include a generous employer pension contribution (up to 12.5% subject to an employee contribution via salary sacrifice of 6%), holiday allowance (33 days, pro rata, including public holidays), private medical insurance, group income protection, life insurance and a 4 week paid sabbatical after 4 years of service. The team is based in Cambridge, UK, amongst the Silicon Fen’s ecosystem of high tech start ups. Our office is in the heart of the city and a short walk from the train station. We do not discriminate against any employee or candidate because of race, colour, sex, age, national origin, religion, sexual orientation, gender identity, disability, or any other protected characteristic. Please let us know if you need any adjustments made to the application or selection process. We will be happy to help. #J-18808-Ljbffr

Company: Cambridge University Hospitals NHS Foundation Trust The post holder will play a central role in shaping CUH's neighbourhood health strategy - helping define future models of care, informing investment decisions, and ensuring that learning from pilots translates into scalable, sustainable change. The role focusu on the Integrated Urgent & Emergency Care portfolio, with the flexibility to support wider programme priorities as required. Working collaboratively with senior stakeholders, the Strategic Project Lead will use data and evidence to guide decision making, and lead the design, testing and scale up of new models of care, addressing operational, cultural and financial barriers. The post holder will maintain an active awareness of the evolving national and local policy landscape, and any implications for our system. The role requires close collaboration with clinical and operational leaders across CUH and our partners, and offers wide exposure to our executive team and board, commissioners, primary/community care, and regional/national teams. Have overall responsibility for developing and managing the operational implementation of strategic projects, including the development of the model and implementation plan (including the use of a risk register, escalating issues when required). Identify and diagnose strategically relevant issues and opportunities (e.g. through horizon scanning), generating solutions, and iterating with relevant stakeholders. Work with Divisional Directors, Finance, Workforce and external partners (e.g. charities), to assess and plan requirements for the delivery of strategies / major projects. Cambridge University Hospitals (CUH) NHS Foundation Trust comprises Addenbrookes Hospital and the Rosie Hospital in Cambridge. With over 13,000 staff and over 1100 beds the priorities of the Trust focus on a quality service which is all about people - patients, staff and partners. Recognised as providing 'outstanding' care to our patients and rated 'Good' overall by the Care Quality Commissioner, is testament to the skill and dedication of the people who work here. CUH's values - Together - Safe, Kind, Excellent - are at the heart of patient care, defining the way all staff work and behave. The Trust provides accessible high-quality healthcare for the local people of Cambridge, together with specialist services, dealing with rare or complex conditions for a regional, national and international population. CUH is committed to promoting a diverse and inclusive community - a place where we can all be ourselves. We value our differences and fully advocate and support an inclusive working environment where every individual can fulfil their potential. We want to ensure our people are truly representative of all the communities that we serve. We welcome applications for all positions in the organisation irrespective of people's age, disability, ethnicity, race, nationality, gender identity, sex, sexual orientation, religion or belief, marriage and civil partnership status, or pregnancy and maternity status or social economic background. Please see the attached Applicant Information Pack (combined Job Description and Person Specification) for key duties and responsibilities. This vacancy will close at midnight on 8th November 2026. Interviews are due to be held on 19th November 2026. All appointments with a start date on or after 1 October 2026 will be subject to a 12-week probationary period. This provides a structured and supportive period to help you settle into your role, with regular reviews and feedback. As there is a 12-week probationary period, successful candidates are expected to have the Right to Work for a period of at least 6 months from the proposed start date. Immigration and Right to Work status will be discussed with successful candidates at conditional offer. CUH will not offer sponsorship to candidates that have applied for a fee waiver. Due to Home Office immigration rules, a full-time permanent vacancy cannot be filled by individuals on a student visa. Therefore, please be advised that if you are a student visa holder, we will not be able to offer you a full-time permanent contract unless you have: • applied for a Graduate visa, • or you will have successfully completed your course and have applied for a Graduate visa before the anticipated start date of your employment As per Home Office guidelines, please be aware that, unfortunately, the Trust is unable to offer sponsorship for this position. At Cambridge University Hospitals, we want to do all we can to support good working days. We offer development opportunities and a wide range of benefits, including on-site leisure facilities, shopping concourse and day nurseries. Our good work programme currently includes providing reduced cost Stagecoach bus travel to and from Cambridge University Hospital site. Park and Ride bus journeys between Babraham Road and Trumpington sites are free, as is the route to and from Cambridge train station and our hospitals. We also subsidise the cost of parking on site for eligible staff. On CUH campus, hot food is available 24/7 and at a reduced cost for colleagues. Recently we launched the first of our staff pod break spaces. Located in the Deakin Centre, we have a purpose-created colleague-only café, with free tea and coffee, a break space and private outside area for colleagues to rest, refuel and recharge. Just one of the ways we are working hard to support good working days at CUH. CUH is committed to assisting employees in achieving a good work-life balance irrespective of role or personal circumstances. Flexible arrangements may include, but are not limited to, part-time working, job-share, term-time working and flexible start and finish times. Please note if you would like to discuss the required hours of this role further, you should approach the contact given. In some cases, alternative working hours will be considered. We welcome applications from the Armed Forces. #J-18808-Ljbffr

Cambridge, Cambridgeshire, United Kingdom What if you could do the kind of work the world needs? At WSP, you can access our global scale, contribute to landmark projects and connect with the brightest minds in your field to do the best work of your life. You can embrace your curiosity in a culture that celebrates new ideas and diverse perspectives. You can experience a world of opportunity and the chance to shape a career as unique as you. • Meeting the requirements of an 'Associate' grade and have a proven track record in both leading projects for your discipline and for an overall project team. Be able to demonstrate where your personal input has led to the commission being won or successfully delivered., • Work in a joint leadership capacity within a mechanical, electrical and public health (MEP) discipline team, providing design and technical advice with a multidisciplinary approach on a wide range of projects., • Able to work using your own initiative with minimal supervision on all technical aspects of their discipline or have specific specialist experience applicable to the role., • Able to work under the clear direction of the client and interpret the needs of the project as a whole to meet the full delivery needs., • Supervise the completion of detailed designs and supervise the work of others in this function., • Consider the feasibility of the project specific to the discipline and lead the overall feasibility study for the project., • Take responsibility for and direct others in the production of detailed and performance specification for the discipline subject to review by colleagues prior to issue., • Reviewing design programmes and ensuring that sufficient resource is available to complete the works Your Team • You will work closely with likeminded individuals on exciting and challenging projects., • Building Services in Cambridge consists of 25 mechanical, electrical, and public health engineers who form part of the National MEP Team of nearly 400 MEP engineers., • We make extraordinary efforts to attract, develop, engage and retain the best professionals in our fields of expertise because this is what makes us great., • We put the highest ethical standards at the centre of all we do, and professionalism is inherent in our offering., • We are humble and act with moral and intellectual integrity, keep our word, treat everyone with respect, support our colleagues, and embrace diversity., • We care about individuals and their progress and offer the most fulfilling career development for our professionals. We promote our young recruits because we believe fresh perspectives bring great ideas and new energy., • You will be based in our Cambridge office, offering easy commuting options via Cambridge Train Station and access into Cambridge City Centre within a 10minute walk. What we will be looking for you to demonstrate… Key Technical Requirements: • Having an understanding of the UK and European regulations applicable to the discipline and how to demonstrate this in project execution., • Have UK project experience within Building Services MEP, • Have working understanding of latest electrical design software packages to allow the allocation / direction of work on projects., • Have understanding of working with Revit MEP or other 3D design software to allow the allocation / direction of work on projects., • Have an ability to sketch and communicate technical detail graphically and effectively., • Have working knowledge of AutoCAD to allow the allocation / direction of work on projects. Electrical Design Skills: • Low voltage standby generation, • UPS systems, • Cable calculations (via software and by hand), • Lighting calculations (via software and by hand), • Small power, • Access control & security systems, • Disabled call systems, • Data cabling and outlets Qualifications: • BEng/Bsc/MEng in applicable engineering discipline, • CIBSE / IET / Engineering Council accredited honours degree or specific experience that will lead to Chartership., • Working towards I Eng or C Eng status, • RevitMEP training applied to the discipline Imagine a better future for you and a better future for us all. Join our close-knit community of talented individuals who share your passion for making a positive impact. Our global team includes more than 83,000 employees, working together to make a difference in communities both close to home and around the world. #LI-CH1 • Job Identification 97304, • Posting Date 10/09/2026, 11:54 AM We are one of the world’s leading engineering and professional services firms. Our passionate people are united by the common purpose of creating positive, long-lasting impacts on the communities we serve through a culture of innovation, integrity, and inclusion. With over 11,000 professionals across the UK and Ireland, we are dedicated to our local communities and propelled by international brainpower. WHAT’S IN IT FOR YOU? Work-life balance At WSP, we understand that work is just one aspect of your life. It’s important to make time for you, your family, friends, interests and your community. Our hybrid working policy offers the flexibility to work from home two days a week, while also providing opportunities to collaborate in our modern offices across the UK. Inclusivity & Belonging We welcome applicants with varied backgrounds and experiences. We enable rewarding careers by encouraging people to bring their whole and authentic selves to work so that our work represents the fullest spectrum of society. We celebrate integrity and treat people with respect, supporting each other and embracing a culture of inclusion and belonging at WSP. We have our employee resource groups bring together employees and allies with different backgrounds to promote a culture of inclusivity. We are committed to supporting our people, giving you the tools to make improvements to your health and wellbeing through our Thrive programme. Our Virtual GP service gives you access to an NHS or Irish Medical Council GP at a time and place that suits you – giving you peace of mind and quick access to medical advice when you need it most. We also provide reasonable workplace adjustments for those in need. Additionally, you can benefit from the Gymflex scheme, which offers up to 40% off annual gym memberships through our WSP flexible benefits program, as well as a comprehensive menopause support package. Flex your time To enhance work-life balance, WSP offers the “WSP My Hour,” allowing you to take one hour each day for personal activities, with the flexibility to make up the time earlier or later that day. We also provide part-time and flexible working arrangements, the option to purchase additional leave, and the ability to use your bank holiday entitlement to suit you. We understand the importance of development and training to you. That’s why we foster a supportive environment that invests in your growth, whether through training, mentoring, or Chartership. #WeAreWSP Here at WSP we positively encourage applications from suitably qualified and eligible candidates regardless of sex, race, disability, age, sexual orientation, gender reassignment, religion or belief, marital status, pregnancy or maternity/paternity. As a Disability Confident leader, we will interview all disabled applicants who meet the essential criteria, please let us know if you require any workplace adjustments in support of your application. #J-18808-Ljbffr

CommonAI CIC is a non-profit membership organisation, founded on a belief in collaborative engineering for the safe and responsible development of foundational AI technologies. A place where AI startups, enterprises large and small, public sector bodies and academia can share resources and knowledge, to codevelop and grow businesses, fast. As a Software Engineer (AI/ML) you will help build smart systems, and their components, that leverage the latest open source artificial intelligence and machine learning technologies to solve real world challenges. Open development is at the heart of what we do and we encourage our engineers to play an active role in open source communities. The ideal candidate will have: • Excellent programming abilities with experience in relevant languages (Python, Rust, C++ and/or Go), • Experience with relevant libraries/tools (PyTorch, Tensorflow, Neo4j) and/or AI accelerators (GPU, TPU) is a plus, • Deep understanding of open source development processes (RFCs/proposals, GitHub pull requests) with demonstrable contributions to open source projects and/or communities, • Exceptional written and verbal communication skills, with the ability to present complex technical concepts clearly to diverse audiences, • Some familiarity with containerization technologies (Docker, Podman, Kubernetes) and cloud (AWS, GCP) application deployment including monitoring (Grafana), • Desire to work in a rapidly evolving startup environment where you will have a high impact, • Strong academic background with at least a Bachelor's degree in a technical field. Postgraduate degrees are preferred and research experience is valued, • A collaborative and supportive work environment, • The opportunity to have a high impact in a growing organisation, • Competitive salary package and pension, • Professional development opportunities, • Networking opportunities with influential people from across the tech sector and academia, • A vibrant office environment located a few minutes walk away from Cambridge train station CommonAI CIC is an equal opportunity employer and is committed to creating an inclusive and diverse workplace. #J-18808-Ljbffr

Are you looking for a new opportunity? What is important for you to have from your next practice? Let’s discuss how we can help you. Are you looking for a new opportunity? What is important for you to have from your next practice? Let’s discuss how we can help you. At Together Dental Cambridge we can offer • Part time locum dentist position with Monday, Tuesday & Thursday available. The practice opening hours are from 8.30am to 5pm, but we can consider different working hours to suit you., • We are able to offer a day rate of £450 with a target of 25 UDAs plus an additional rate for overachieving the target, • Any private work completed will be paid at a 50% private rate., • We have an inclusive, friendly and supportive team to enable you to deliver excellent patient care. You will be joining a team with the superb support of: • Our established patient base, • Access to our clinical support group, • Available Regional Manager and Clinical Director, • Support with any training to increase the services you offer (possible financial help too), • Lead clinician on hand to help discuss cases and patient issues and actively encourage this., • Access to supporting specialists for more complex cases, • We have a successful and robust internal marketing system which actively promotes individual dentist's special interests to direct patients for specific treatments. Together Dental Cambridge is a fully equipped 3 surgery mixed NHS and private practice based in the centre of Cambridge which is a lovely busy city. Fully air conditioned with parking nearby and not far away from the train station. Our surgeries have a wide range of high quality equipment and materials ensuring that you have the clinical freedom to offer high quality standards of patient care. • Fully computerised with modern well-equipped surgeries in a large practice, • A dedicated staff room with kitchen facilities and lockers, • An excellent team morale with long standing members both clinical and non‑clinical, • An ethos of collaboration - we actively ask the dentists/hygienists to discuss cases with or without patients present for second opinions, advice, treatment planning or even assistance, • A dedicated management structure so issues are sorted swiftly and with minimal disruption to the working day, • Digital X-rays, • Dentally with patient portal and online booking and payment system, • Rotary Endodontic Equipment with apex locators for both NHS and Private care, • iTero, • Implant Motor, • Airflow, • An excellent standard of equipment which is regularly serviced and maintained, • An excellent selection of materials readily available and stocked, • Air-conditioned and air purifiers in surgeries, • An excellent hygienist operating two days each week who works closely with the dentists, • Access to treatments including composite bonding, implants and Invisalign You can find out more about the practice here: Homepage - Together Dental Cambridge Together Dental is a growing group of practices led by forward thinking, caring dentists with a shared vision to provide outstanding dental care for both NHS and private patients. Whether you are an outstanding dental professional or would like to join one of our skilled support teams, we would like to hear from you. We are passionate about providing support to ensure time is used where it is best spent, with our patients. Patient care is always at the heart of everything we do. We aim to create the perfect platform to provide care, whilst encouraging shared learning through peer-review, case-based discussion and mutual support. Here are some of our useful videos that will also help you get a feel for our group and an insight to some of our existing clinicians and patient feedback: channel/UCVZ-ZAPTCgDFHyeiQ5D1iAg/videos Together Dental is a growing group of practices led by forward thinking, caring dentists with a shared vision to provide outstanding dental care for both NHS and private patients. We value the trust that our patients have placed in us and work hard to protect that trust. Our team are passionate about working together to deliver the best possible care in a friendly and welcoming environment. We support each other, providing outstanding service to practices and our dentists. We educate our patients empowering them to make informed decisions about their oral health. There is a sense of energy and purpose behind everything we do. We instil trust in our teams and patients via our empathetic and supportive approach. Previous Next #J-18808-Ljbffr

About lowRISC lowRISC CIC is a non-profit Community Interest Company, collaborating with international engineering partners to create and maintain open source silicon designs, firmware and tools. This includes lowRISC’s flagship OpenTitan project - the largest open source silicon project in the world. Through projects like OpenTitan we partner with organisations such as ETH Zürich, G+D Mobile Security, Google and Nuvoton. We also maintain close links to researchers in computer architecture at the University of Cambridge amongst other research communities. About the role We are looking for an experienced Technical Project Manager to join our engineering team and deliver complex silicon subsystem and chip design projects. This is an hybrid/on-site hands-on role based in our Cambridge office, working closely with both our technical teams and major commercial semiconductor partners to coordinate the delivery of high-quality open source silicon designs, firmware, and tools. Key responsibilities Plan, track, and manage complex subsystem and chip design projects for major commercial customers. Apply structured project/programme planning tools, working closely with engineering teams on silicon and embedded software projects. Align internal digital design, verification, and embedded software teams with external silicon partners and academic collaborators. Collaborate with engineering leads to draft and execute Statements of Work (SoWs), manage technical scope changes, and run robust risk tracking. Manage tracking and compliance for grant-funded research and development projects. Required experience Bachelor's degree in a technical field or equivalent practical experience. At least 3 years project management experience in highly technical environments. Strong understanding of good practices in planning and managing delivery in technically challenging projects. Strong grasp of hardware development processes of Embedded Systems including digital design, FPGA development, silicon design flows or firmware / software. Experience working closely with technical teams and facilitating cross-functional collaboration. Strong relationship management abilities, excellent communication and interpersonal skills. Desirable experience Professional certification in project management (APM, PMP or equivalent). * Familiarity with open-source silicon communities and collaborative governance. Salary, Benefits, and Sponsorship Starting salary is £70-90,000 GBP depending on experience. Benefits include a generous employer pension contribution (up to 12.5%), holiday allowance (33 days including public holidays), private medical insurance, and a 4 week paid sabbatical after 4 years of service. The team is based in Cambridge, UK, amongst the Silicon Fen’s ecosystem of high tech startups. Our office is in the heart of the city and a short walk from the train station. We do not discriminate against any employee or candidate because of race, colour, sex, age, national origin, religion, sexual orientation, gender identity, disability, or any other protected characteristic. #J-18808-Ljbffr

CommonAI CIC is a non-profit organisation built around collaborative engineering and is backed by a mix of UK Government and private funding in order to design, build and deploy innovative AI systems. Our Scaling Inference Lab (SIL) is developing simulation grounded in real measurements to help the UK invest in promising AI infrastructure. We connect proposed hardware designs to realistic workloads, helping investors and engineers understand their potential, their limitations and the next experiment worth doing. Build the workloads and benchmarks that reveal how AI infrastructure performs on useful applications. You will develop reproducible LLM serving workloads, connecting application behaviour and requirements to physical measurements and SIL's simulation platform. The initial focus includes long-context inference and agentic workloads. You will investigate how request patterns, context lengths, caching, repeated model calls and interactions with tools shape the demands placed on a system. Your benchmarks will make performance comparisons meaningful by specifying the work being done and the quality and service requirements it must meet. Working with measurement and simulation engineers and our partners, you will help preserve the workload behaviour that matters when moving between real systems and models. Your work will help engineers and investors understand which infrastructure choices suit particular applications, and how conclusions change when the workload changes. • Build and maintain reproducible LLM serving workloads, serving configurations and benchmark tools, including long-context and agentic workloads, using suitable existing software., • Characterise relevant behaviour such as request arrivals, prompt and output lengths, caching, batching, tool interactions and dependencies between requests., • Define performance measures alongside application-quality and service requirements, so comparisons reflect useful work under stated conditions., • Work with measurement engineers to reproduce workloads on physical systems, and with simulator engineers to develop workload representations that preserve the behaviour and dependencies that matter., • Investigate how changes in workloads and serving configurations affect conclusions, and keep the benchmark suite relevant as inference technology and workloads evolve., • Document datasets, model and software versions, configurations, provenance and permitted use so other engineers can reproduce and extend the work., • An example early project would be to build a reproducible agentic workload and work with measurement colleagues to compare its performance across two systems, under agreed application-quality and service requirements., • Experience building or evaluating ML systems, inference services, performance benchmarks or comparable workload-driven systems., • Strong practical software skills for constructing reliable benchmark tools, analysing results and debugging interactions between applications and infrastructure., • Understanding of LLM inference and an interest in how serving choices affect performance and output quality., • Care in designing reproducible comparisons and interpreting results against stated application requirements., • Practical experience using AI coding agents, with the judgement to validate generated code, data transformations and evaluations., • Experience with vLLM, SGLang or similar serving software, long-context workloads, agentic applications, trace analysis or quality evaluation would be valuable. Depth in a subset is sufficient. The initial remit is LLM serving; other forms of inference are possible later extensions., • Help build UK AI capability and improve how investment and compute serve the public good., • Build a network spanning UK hardware SMEs, major technology companies, venture and public investors, and leading universities., • Shape a growing team with startup pace and close academic links., • Use frontier models and coding agents with a substantial tooling budget., • A collaborative and supportive work environment, • The opportunity to have a high impact in a growing organisation, • Competitive salary package, including pension and benefits, • Professional development opportunities, • Networking opportunities with influential figures from across the tech sector and academia, • A vibrant office environment located a few minutes’ walk away from Cambridge train station Hybrid, with Cambridge attendance agreed individually around collaboration and the work; applicants across the UK are welcome. We are open to permanent employment, part-time arrangements and consultancy. We would like you to start as soon as practical and can be flexible for the right person. We particularly welcome experienced engineers and can also consider earlier-career candidates with strong evidence of ability. Relevant postgraduate study is useful, but qualifications are secondary to experience and the quality of your work. #J-18808-Ljbffr

CommonAI CIC is redefining deep-tech AI solutions through collaborative engineering and shared infrastructure. Working at the intersection of AI, cloud and large-scale infrastructure, we partner with commercial, government and research entities to accelerate AI adoption across the UK and Europe. Backed by UK Government and private funding, our team of experienced founders and engineers is building the future of AI systems. Its members include ARIA, Barclays and a fast-growing group of deeptech startups. Highly regulated industries like financial services need guaranteed, predictable outcomes that today's large language models cannot provide on their own. Our High Assurance programme will develop AI systems which can be used safely in environments where accuracy and explainability are critical building blocks in creating a required level of trust. The programme is exploring several approaches, including: • Formal methods and verification: proofs that an AI system must satisfy before it can act, • Domain-specific small language models for regulated use cases, • AI control platforms that govern how AI systems behave in production The programme launched recently, with Barclays as its lead member and held its first workshop, "Towards Trustworthy Harnesses", with members, academics and domain experts. We are looking for a Programme Technical Lead to drive execution of the High Assurance AI Programme. The programme already has an academic architectural lead; this role turns the programme's research direction into delivered engineering, working closely with the architectural lead, our Chief Science Officer and our members. You will report to the CTO and build and lead the programme's engineering team. • Own delivery of the programme's technical roadmap, from research direction to working infrastructure, • Build, hire and lead an engineering team based in Cambridge, • Translate the needs of regulated industries into clear technical requirements, making sure the programme solves the problems which matter to members, • Bring members, researchers and partners together around a shared roadmap, • Work with the architectural lead to evaluate approaches such as formal methods, AI control and small language models and decide what to build, • Represent the programme technically with members and partners We do not expect anyone to have all of the experience below. Deep expertise in one area, combined with the ability to work across the others, matters more than ticking every box. • Understanding/experience of processes/workflows (and their associated constraints) in the financial services industry, particularly banks, • Understanding of how AI is or might be used, governed or assured in a regulated industry, ideally financial services (for example model risk, AI governance or controls), • Experience with PyTorch or other similar frameworks, • Enough technical depth to engage credibly with ML researchers and understand the problems the programme needs to solve, without needing to be the person who writes the proofs or algorithms, • Ability to bring together organisations with different priorities and build a shared plan, • Experience leading an engineering team and driving delivery in a technical, research-driven environment, • Experience with formal methods, verification, AI safety, AI assurance or AI control, • Experience with small or domain-specific language models, • Relationships with banks, insurers or regulators, • Experience in a startup, or building a new product, or independent project / research that has resulted in software used by others, • Lead a new programme at the boundary of research and industry, helping to solve tough challenges such as how AI can be used safely in critical industries, • High-impact role in a rapidly growing, well-funded organisation, • Mission-led work for the public good, released over time as managed open source, • Competitive salary, pension, healthcare and benefits, including company share scheme, • Networking opportunities with leaders across tech and academia, • Vibrant office situated near to Cambridge train station CommonAI CIC is an equal opportunity employer and is committed to creating an inclusive and diverse workplace. #J-18808-Ljbffr

At lowRISC we believe that open source development can transform the semiconductor industry. To help achieve this vision of production-ready, commercial-grade chips and silicon building blocks, we employ a software team with a wide range of skills. Our software engineers work across the full software stack, from firmware and toolchains that interact directly with hardware to cloud-based infrastructure that tests our products at scale. This includes work in areas such as: • RISC-V firmware and driver development., • Software hardening and security tools., • Embedded operating systems (in Rust and C)., • Compilers, code generators, and linters., • Simulators and emulators (e.g. QEMU)., • Electronic design automation (EDA) tools., • Performance and code size analysis., • Cloud-based infrastructure (such as continuous integration pipelines). lowRISC is a non-profit Community Interest Company. With a business model that includes member fees and engineering services, we have shown that it is possible to run world-leading projects collaboratively for everyone’s benefit. Originally a spinout from Cambridge University’s Computer Lab, our UK team is based in Cambridge and our Switzerland team is based in Zurich. Our Ibex CPU and OpenTitan Root of Trust projects have been taken to production silicon, partnering with world-leading organisations including Google, combining open-source approaches with best-practice chip design methodologies. • Solid experience with C or Rust., • Strong algorithmic and problem solving skills., • A degree in Computer Science or equivalent experience., • A high standard of written and spoken English., • Experience leading a project or another major initiative, in which you had to communicate and negotiate requirements with multiple stakeholders., • Strong knowledge of computer architecture; some experience with assembly languages (x86, RISC-V)., • Familiarity with C++ and Python., • Experience with build systems and reproducible tooling (e.g. Bazel, Nix, uv)., • Experience developing for embedded or resource-constrained environments, including real-time systems., • Familiarity with CI/CD for embedded or systems software., • A good understanding of compilers and linkers; experience with LLVM is a plus., • Good knowledge of Linux., • Experience contributing to open source projects., • Higher degree levels and academic research experience are highly valued., • Salary dependent upon experience., • generous employer pension contribution (up to 12.5% subject to an employee contribution via salary sacrifice of 6%), • holiday allowance (33 days, pro rata, including public holidays), • private medical insurance, • group income protection, • life insurance, • a 4 week paid sabbatical after 4 years of service The team is based in Cambridge, UK, amongst the Silicon Fen’s ecosystem of high tech start ups. Our office is in the heart of the city and a short walk from the train station. We do not discriminate against any employee or candidate because of race, colour, sex, age, national origin, religion, sexual orientation, gender identity, disability, or any other protected characteristic. Please let us know if you need any adjustments made to the application or selection process. We will be happy to help. #J-18808-Ljbffr

CommonAI CIC is a non-profit organisation built around collaborative engineering and is backed by a mix of UK Government and private funding in order to design, build and deploy innovative AI systems. Our Scaling Inference Lab (SIL) is developing simulation grounded in real measurements to help the UK invest in promising AI infrastructure. We connect proposed hardware designs to realistic workloads, helping investors and engineers understand their potential, their limitations and the next experiment worth doing. Design and run experiments that reveal how AI infrastructure behaves, what limits its performance and how reliably that behaviour can be predicted. You will build measurement tools and turn observations of real hardware and software into evidence for SIL’s simulation platform and the investment decisions it supports. Your work will range from targeted experiments on individual components to measurements of complete LLM serving systems. Working with workload, simulation and uncertainty specialists, you will investigate bottlenecks, explain performance variation and identify the meaningful effects. We welcome deep expertise in one or more parts of the hardware and software stack. You will work with colleagues who bring complementary strengths, with scope to broaden your expertise as the team grows. • Design and run reproducible experiments, from individual hardware or software components to complete LLM serving systems, covering relevant workloads, configurations and operating conditions., • Develop reusable measurement tools and collect traces, profiles and counters to characterise latency, throughput, memory behaviour and, where reliable measurement is possible, power and energy., • Investigate bottlenecks, performance variation and unexpected behaviour across the layers your expertise covers, working with colleagues to extend the investigation through the stack., • Take responsibility for reproducible workload details, software versions, configuration and measurement methods so results can be reproduced, compared and used to calibrate simulator models., • Work with simulation and uncertainty specialists to choose measurements that resolve important unknowns and undertake curation of calibration and validation data., • Help define the controls needed for experiments and use the resulting cluster-control interfaces, coordinating access and changes with colleagues responsible for the hardware. An example early project would be to work with workload colleagues to establish a reproducible reference experiment and measurement dataset, investigating how performance changes under a selected configuration or load change. • Experience investigating the performance of real computing systems and designing experiments that support rigorous conclusions., • Depth in one or more relevant areas: accelerator profiling, inference runtimes, memory systems, distributed/HPC workloads, networking, or power and energy measurement., • The ability to build maintainable automation and analysis tools in a suitable software language, and to learn unfamiliar profiling tools., • Knowledge and care of experimental controls, repeatability, confounding effects and the limits of a measurement., • Some experience using AI coding agents, alongside sound debugging and review habits. LLM serving experience and familiarity with tools such as Nsight, PyTorch Profiler or system/network profilers would be useful. These are examples, not a requirement to know every tool. Strong expertise in one part of the stack is welcome; we can shape the role and recruit complementary strengths around it. • Help build UK AI capability and improve how investment and compute serve the public good., • Build a network spanning UK hardware SMEs, major technology companies, venture and public investors, and leading universities., • Shape a growing team with startup pace and close academic links., • Use frontier models and coding agents with a substantial tooling budget., • A collaborative and supportive work environment, • The opportunity to have a high impact in a growing organisation, • Competitive salary package, including pension and benefits, • Professional development opportunities, • Networking opportunities with influential figures from across the tech sector and academia, • A vibrant office environment located a few minutes’ walk away from Cambridge train station Hybrid, with regular time in Cambridge agreed individually around hardware work and collaboration; applicants across the UK are welcome. We are open to permanent employment, part-time arrangements and consultancy. We would like you to start as soon as practical and can be flexible for the right person. We particularly welcome experienced engineers but will also consider earlier-career candidates with strong evidence of ability. Relevant postgraduate study is useful, but qualifications are secondary to experience and the quality of your work. CommonAI CIC is an equal opportunity employer and is committed to creating an inclusive and diverse workplace. #J-18808-Ljbffr

CommonAI CIC is a non-profit organisation built around collaborative engineering and is backed by a mix of UK Government and private funding in order to design, build and deploy innovative AI systems. Our Scaling Inference Lab (SIL) is developing simulation grounded in real measurements to help the UK invest in promising AI infrastructure. We connect proposed hardware designs to realistic workloads, helping investors and engineers understand their potential, their limitations and the next experiment worth doing. Help evaluate proposed AI hardware before it is available as a finished chip. You will turn RTL designs into working FPGA prototypes and emulation experiments, giving SIL and its technology partners a practical way to explore design behaviour and architectural choices. The initial focus is on RTL prototyping and emulation. You will help choose a useful first design and platform, adapt and integrate the RTL, and establish repeatable build, bring-up and experimental workflows. Working with our partners and colleagues in software and simulation, you will connect observations from the prototypes to wider system studies. Your work will help establish what the experiments tell us about a proposed design, how those findings relate to simulator models and where further evidence is needed. • Adapt and integrate RTL for FPGA prototyping or emulation, working with the design owners and documenting changes needed for the prototype, • Develop and maintain synthesis, implementation, timing-closure and reproducible build flows for the selected platform, • Bring up and debug designs, developing tests and instrumentation to check functional behaviour and support repeatable experiments, • Develop or integrate the control, stimulus and data-capture facilities needed for the agreed experiments, working with software colleagues on host interfaces and integration, • Relate observations from emulation to the model being evaluated, explaining how prototype frequency, interface overheads and other limitations affect interpretation, • Help assess platform, toolchain and hardware requirements, and document workflows that colleagues and partners can reuse An example early project would be to take a partner's RTL design through FPGA implementation and bring-up, then instrument it to investigate a selected architectural choice and compare observations with a simulator model. • Hands-on experience taking RTL through an FPGA prototyping or emulation flow and debugging the resulting system, • Strong digital-design skills in Verilog, SystemVerilog, VHDL or an equivalent HDL, together with practical timing and verification judgement, • Experience with a relevant FPGA or emulation toolchain and the scripting needed to make builds and experiments repeatable, • The ability to collaborate across hardware and software boundaries and explain the conclusions and limitations of a prototype experiment, • Practical experience using AI coding agents, with the judgement to validate generated HDL, scripts and software Experience with processor or accelerator designs, multi-FPGA partitioning, host interfaces or performance characterisation would be useful. Depth with a relevant platform is sufficient; the team will choose the initial platform and design around the first useful study. • Help build UK AI capability and improve how investment and compute serve the public good, • Build a network spanning UK hardware SMEs, major technology companies, venture and public investors, and leading universities, • Shape a growing team with startup pace and close academic links, • Use frontier models and coding agents with a substantial tooling budget, • A collaborative and supportive work environment, • The opportunity to have a high impact in a growing organisation, • Competitive salary package, including pension and benefits, • Professional development opportunities, • Networking opportunities with influential figures from across the tech sector and academia, • A vibrant office environment located a few minutes' walk away from Cambridge train station Hybrid, with Cambridge attendance agreed individually around collaboration and the work; applicants across the UK are welcome. We are open to permanent employment, part-time arrangements and consultancy. We would like you to start as soon as practical and can be flexible for the right person. We particularly welcome experienced engineers and can also consider earlier-career candidates with strong evidence of ability. Relevant postgraduate study is useful, but qualifications are secondary to experience and the quality of your work. CommonAI CIC is an equal opportunity employer and is committed to creating an inclusive and diverse workplace. #J-18808-Ljbffr

About CommonAI CommonAI CIC is a non-profit organisation built around collaborative engineering and is backed by a mix of UK Government and private funding in order to design, build and deploy innovative AI systems. Our Scaling Inference Lab (SIL) is developing simulation grounded in real measurements to help the UK invest in promising AI infrastructure. We connect proposed hardware designs to realistic workloads, helping investors and engineers understand their potential, their limitations and the next experiment worth doing. The Opportunity Build the workloads and benchmarks that reveal how AI infrastructure performs on useful applications. You will develop reproducible LLM serving workloads, connecting application behaviour and requirements to physical measurements and SIL's simulation platform. The initial focus includes long-context inference and agentic workloads. You will investigate how request patterns, context lengths, caching, repeated model calls and interactions with tools shape the demands placed on a system. Your benchmarks will make performance comparisons meaningful by specifying the work being done and the quality and service requirements it must meet. Working with measurement and simulation engineers and our partners, you will help preserve the workload behaviour that matters when moving between real systems and models. Your work will help engineers and investors understand which infrastructure choices suit particular applications, and how conclusions change when the workload changes. What you will do • Build and maintain reproducible LLM serving workloads, serving configurations and benchmark tools, including long-context and agentic workloads, using suitable existing software., • Characterise relevant behaviour such as request arrivals, prompt and output lengths, caching, batching, tool interactions and dependencies between requests., • Define performance measures alongside application-quality and service requirements, so comparisons reflect useful work under stated conditions., • Work with measurement engineers to reproduce workloads on physical systems, and with simulator engineers to develop workload representations that preserve the behaviour and dependencies that matter., • Investigate how changes in workloads and serving configurations affect conclusions, and keep the benchmark suite relevant as inference technology and workloads evolve., • Document datasets, model and software versions, configurations, provenance and permitted use so other engineers can reproduce and extend the work. An example early project would be to build a reproducible agentic workload and work with measurement colleagues to compare its performance across two systems, under agreed application-quality and service requirements. Requirements • Experience building or evaluating ML systems, inference services, performance benchmarks or comparable workload-driven systems., • Strong practical software skills for constructing reliable benchmark tools, analysing results and debugging interactions between applications and infrastructure., • Understanding of LLM inference and an interest in how serving choices affect performance and output quality., • Care in designing reproducible comparisons and interpreting results against stated application requirements., • Practical experience using AI coding agents, with the judgement to validate generated code, data transformations and evaluations. Experience with vLLM, SGLang or similar serving software, long-context workloads, agentic applications, trace analysis or quality evaluation would be valuable. Depth in a subset is sufficient. The initial remit is LLM serving; other forms of inference are possible later extensions. Why join us • Help build UK AI capability and improve how investment and compute serve the public good., • Build a network spanning UK hardware SMEs, major technology companies, venture and public investors, and leading universities., • Shape a growing team with startup pace and close academic links., • Use frontier models and coding agents with a substantial tooling budget. Benefits • A collaborative and supportive work environment, • The opportunity to have a high impact in a growing organisation, • Competitive salary package, including pension and benefits, • Professional development opportunities, • Networking opportunities with influential figures from across the tech sector and academia, • A vibrant office environment located a few minutes’ walk away from Cambridge train station Working with us Hybrid, with Cambridge attendance agreed individually around collaboration and the work; applicants across the UK are welcome. We are open to permanent employment, part-time arrangements and consultancy. We would like you to start as soon as practical and can be flexible for the right person. We particularly welcome experienced engineers and can also consider earlier-career candidates with strong evidence of ability. Relevant postgraduate study is useful, but qualifications are secondary to experience and the quality of your work. CommonAI CIC is an equal opportunity employer and is committed to creating an inclusive and diverse workplace. #J-18808-Ljbffr

CommonAI CIC is a non-profit organisation built around collaborative engineering and is backed by a mix of UK Government and private funding in order to design, build and deploy innovative AI systems. Our Scaling Inference Lab (SIL) is developing simulation grounded in real measurements to help the UK invest in promising AI infrastructure. We connect proposed hardware designs to realistic workloads, helping investors and engineers understand their potential, their limitations and the next experiment worth doing. Build the workloads and benchmarks that reveal how AI infrastructure performs on useful applications. You will develop reproducible LLM serving workloads, connecting application behaviour and requirements to physical measurements and SIL's simulation platform. The initial focus includes long-context inference and agentic workloads. You will investigate how request patterns, context lengths, caching, repeated model calls and interactions with tools shape the demands placed on a system. Your benchmarks will make performance comparisons meaningful by specifying the work being done and the quality and service requirements it must meet. Working with measurement and simulation engineers and our partners, you will help preserve the workload behaviour that matters when moving between real systems and models. Your work will help engineers and investors understand which infrastructure choices suit particular applications, and how conclusions change when the workload changes. • Build and maintain reproducible LLM serving workloads, serving configurations and benchmark tools, including long-context and agentic workloads, using suitable existing software, • Characterise relevant behaviour such as request arrivals, prompt and output lengths, caching, batching, tool interactions and dependencies between requests, • Define performance measures alongside application-quality and service requirements, so comparisons reflect useful work under stated conditions, • Work with measurement engineers to reproduce workloads on physical systems, and with simulator engineers to develop workload representations that preserve the behaviour and dependencies that matter, • Investigate how changes in workloads and serving configurations affect conclusions, and keep the benchmark suite relevant as inference technology and workloads evolve, • Document datasets, model and software versions, configurations, provenance and permitted use so other engineers can reproduce and extend the work, • Experience building or evaluating ML systems, inference services, performance benchmarks or comparable workload-driven systems, • Strong practical software skills for constructing reliable benchmark tools, analysing results and debugging interactions between applications and infrastructure, • Understanding of LLM inference and an interest in how serving choices affect performance and output quality, • Care in designing reproducible comparisons and interpreting results against stated application requirements, • Practical experience using AI coding agents, with the judgement to validate generated code, data transformations and evaluations Experience with vLLM, SGLang or similar serving software, long-context workloads, agentic applications, trace analysis or quality evaluation would be valuable. Depth in a subset is sufficient. The initial remit is LLM serving; other forms of inference are possible later extensions. • Help build UK AI capability and improve how investment and compute serve the public good, • Build a network spanning UK hardware SMEs, major technology companies, venture and public investors, and leading universities, • Shape a growing team with startup pace and close academic links, • Use frontier models and coding agents with a substantial tooling budget, • A collaborative and supportive work environment, • The opportunity to have a high impact in a growing organisation, • Competitive salary package, including pension and benefits, • Professional development opportunities, • Networking opportunities with influential figures from across the tech sector and academia, • A vibrant office environment located a few minutes' walk away from Cambridge train station Hybrid, with Cambridge attendance agreed individually around collaboration and the work; applicants across the UK are welcome. We are open to permanent employment, part-time arrangements and consultancy. We would like you to start as soon as practical and can be flexible for the right person. We particularly welcome experienced engineers and can also consider earlier-career candidates with strong evidence of ability. Relevant postgraduate study is useful, but qualifications are secondary to experience and the quality of your work. CommonAI CIC is an equal opportunity employer and is committed to creating an inclusive and diverse workplace. #J-18808-Ljbffr

About CommonAI CommonAI CIC is a non-profit organisation built around collaborative engineering and is backed by a mix of UK Government and private funding in order to design, build and deploy innovative AI systems. Our Scaling Inference Lab (SIL) is developing simulation grounded in real measurements to help the UK invest in promising AI infrastructure. We connect proposed hardware designs to realistic workloads, helping investors and engineers understand their potential, their limitations and the next experiment worth doing. The Opportunity Design and run experiments that reveal how AI infrastructure behaves, what limits its performance and how reliably that behaviour can be predicted. You will build measurement tools and turn observations of real hardware and software into evidence for SIL’s simulation platform and the investment decisions it supports. Your work will range from targeted experiments on individual components to measurements of complete LLM serving systems. Working with workload, simulation and uncertainty specialists, you will investigate bottlenecks, explain performance variation and identify the meaningful effects. We welcome deep expertise in one or more parts of the hardware and software stack. You will work with colleagues who bring complementary strengths, with scope to broaden your expertise as the team grows. What you will do • Design and run reproducible experiments, from individual hardware or software components to complete LLM serving systems, covering relevant workloads, configurations and operating conditions., • Develop reusable measurement tools and collect traces, profiles and counters to characterise latency, throughput, memory behaviour and, where reliable measurement is possible, power and energy., • Investigate bottlenecks, performance variation and unexpected behaviour across the layers your expertise covers, working with colleagues to extend the investigation through the stack., • Take responsibility for reproducible workload details, software versions, configuration and measurement methods so results can be reproduced, compared and used to calibrate simulator models., • Work with simulation and uncertainty specialists to choose measurements that resolve important unknowns and undertake curation of calibration and validation data., • Help define the controls needed for experiments and use the resulting cluster-control interfaces, coordinating access and changes with colleagues responsible for the hardware. An example early project would be to work with workload colleagues to establish a reproducible reference experiment and measurement dataset, investigating how performance changes under a selected configuration or load change. • Experience investigating the performance of real computing systems and designing experiments that support rigorous conclusions., • Depth in one or more relevant areas: accelerator profiling, inference runtimes, memory systems, distributed/HPC workloads, networking, or power and energy measurement., • The ability to build maintainable automation and analysis tools in a suitable software language, and to learn unfamiliar profiling tools., • Knowledge and care of experimental controls, repeatability, confounding effects and the limits of a measurement., • Some experience using AI coding agents, alongside sound debugging and review habits. LLM serving experience and familiarity with tools such as Nsight, PyTorch Profiler or system/network profilers would be useful. These are examples, not a requirement to know every tool. Strong expertise in one part of the stack is welcome; we can shape the role and recruit complementary strengths around it. Why join us • Help build UK AI capability and improve how investment and compute serve the public good., • Build a network spanning UK hardware SMEs, major technology companies, venture and public investors, and leading universities., • Shape a growing team with startup pace and close academic links., • Use frontier models and coding agents with a substantial tooling budget., • A collaborative and supportive work environment, • The opportunity to have a high impact in a growing organisation, • Competitive salary package, including pension and benefits, • Professional development opportunities, • Networking opportunities with influential figures from across the tech sector and academia, • A vibrant office environment located a few minutes’ walk away from Cambridge train station Working with us Hybrid, with regular time in Cambridge agreed individually around hardware work and collaboration; applicants across the UK are welcome. We are open to permanent employment, part-time arrangements and consultancy. We would like you to start as soon as practical and can be flexible for the right person. We particularly welcome experienced engineers but will also consider earlier-career candidates with strong evidence of ability. Relevant postgraduate study is useful, but qualifications are secondary to experience and the quality of your work. CommonAI CIC is an equal opportunity employer and is committed to creating an inclusive and diverse workplace. #J-18808-Ljbffr

CommonAI CIC is a non-profit organisation built around collaborative engineering and is backed by a mix of UK Government and private funding in order to design, build and deploy innovative AI systems. Our Scaling Inference Lab (SIL) is developing simulation grounded in real measurements to help the UK invest in promising AI infrastructure. We connect proposed hardware designs to realistic workloads, helping investors and engineers understand their potential, their limitations and the next experiment worth doing. Help evaluate proposed AI hardware before it is available as a finished chip. You will turn RTL designs into working FPGA prototypes and emulation experiments, giving SIL and its technology partners a practical way to explore design behaviour and architectural choices. The initial focus is on RTL prototyping and emulation. You will help choose a useful first design and platform, adapt and integrate the RTL, and establish repeatable build, bring-up and experimental workflows. Working with our partners and colleagues in software and simulation, you will connect observations from the prototypes to wider system studies. Your work will help establish what the experiments tell us about a proposed design, how those findings relate to simulator models and where further evidence is needed. • Adapt and integrate RTL for FPGA prototyping or emulation, working with the design owners and documenting changes needed for the prototype., • Develop and maintain synthesis, implementation, timing-closure and reproducible build flows for the selected platform., • Bring up and debug designs, developing tests and instrumentation to check functional behaviour and support repeatable experiments., • Develop or integrate the control, stimulus and data-capture facilities needed for the agreed experiments, working with software colleagues on host interfaces and integration., • Relate observations from emulation to the model being evaluated, explaining how prototype frequency, interface overheads and other limitations affect interpretation., • Help assess platform, toolchain and hardware requirements, and document workflows that colleagues and partners can reuse., • An example early project would be to take a partner's RTL design through FPGA implementation and bring-up, then instrument it to investigate a selected architectural choice and compare observations with a simulator model., • Hands-on experience taking RTL through an FPGA prototyping or emulation flow and debugging the resulting system., • Strong digital-design skills in Verilog, SystemVerilog, VHDL or an equivalent HDL, together with practical timing and verification judgement., • Experience with a relevant FPGA or emulation toolchain and the scripting needed to make builds and experiments repeatable., • The ability to collaborate across hardware and software boundaries and explain the conclusions and limitations of a prototype experiment., • Practical experience using AI coding agents, with the judgement to validate generated HDL, scripts and software., • Experience with processor or accelerator designs, multi-FPGA partitioning, host interfaces or performance characterisation would be useful. Depth with a relevant platform is sufficient; the team will choose the initial platform and design around the first useful study., • Help build UK AI capability and improve how investment and compute serve the public good., • Build a network spanning UK hardware SMEs, major technology companies, venture and public investors, and leading universities., • Shape a growing team with startup pace and close academic links., • Use frontier models and coding agents with a substantial tooling budget., • A collaborative and supportive work environment, • The opportunity to have a high impact in a growing organisation, • Competitive salary package, including pension and benefits, • Professional development opportunities, • Networking opportunities with influential figures from across the tech sector and academia, • A vibrant office environment located a few minutes' walk away from Cambridge train station Hybrid, with Cambridge attendance agreed individually around collaboration and the work; applicants across the UK are welcome. We are open to permanent employment, part-time arrangements and consultancy. We would like you to start as soon as practical and can be flexible for the right person. We particularly welcome experienced engineers and can also consider earlier-career candidates with strong evidence of ability. Relevant postgraduate study is useful, but qualifications are secondary to experience and the quality of your work. CommonAI CIC is an equal opportunity employer and is committed to creating an inclusive and diverse workplace. #J-18808-Ljbffr

About CommonAI CommonAI CIC is a non-profit organisation built around collaborative engineering and is backed by a mix of UK Government and private funding in order to design, build and deploy innovative AI systems. Our Scaling Inference Lab (SIL) is developing simulation grounded in real measurements to help the UK invest in promising AI infrastructure. We connect proposed hardware designs to realistic workloads, helping investors and engineers understand their potential, their limitations and the next experiment worth doing. The Opportunity Help evaluate proposed AI hardware before it is available as a finished chip. You will turn RTL designs into working FPGA prototypes and emulation experiments, giving SIL and its technology partners a practical way to explore design behaviour and architectural choices. The initial focus is on RTL prototyping and emulation. You will help choose a useful first design and platform, adapt and integrate the RTL, and establish repeatable build, bring-up and experimental workflows. Working with our partners and colleagues in software and simulation, you will connect observations from the prototypes to wider system studies. Your work will help establish what the experiments tell us about a proposed design, how those findings relate to simulator models and where further evidence is needed. What you will do • Adapt and integrate RTL for FPGA prototyping or emulation, working with the design owners and documenting changes needed for the prototype., • Develop and maintain synthesis, implementation, timing-closure and reproducible build flows for the selected platform., • Bring up and debug designs, developing tests and instrumentation to check functional behaviour and support repeatable experiments., • Develop or integrate the control, stimulus and data-capture facilities needed for the agreed experiments, working with software colleagues on host interfaces and integration., • Relate observations from emulation to the model being evaluated, explaining how prototype frequency, interface overheads and other limitations affect interpretation., • Help assess platform, toolchain and hardware requirements, and document workflows that colleagues and partners can reuse. An example early project would be to take a partner's RTL design through FPGA implementation and bring-up, then instrument it to investigate a selected architectural choice and compare observations with a simulator model. • Hands-on experience taking RTL through an FPGA prototyping or emulation flow and debugging the resulting system., • Strong digital-design skills in Verilog, SystemVerilog, VHDL or an equivalent HDL, together with practical timing and verification judgement., • Experience with a relevant FPGA or emulation toolchain and the scripting needed to make builds and experiments repeatable., • The ability to collaborate across hardware and software boundaries and explain the conclusions and limitations of a prototype experiment., • Practical experience using AI coding agents, with the judgement to validate generated HDL, scripts and software. Experience with processor or accelerator designs, multi-FPGA partitioning, host interfaces or performance characterisation would be useful. Depth with a relevant platform is sufficient; the team will choose the initial platform and design around the first useful study. Why join us • Help build UK AI capability and improve how investment and compute serve the public good., • Build a network spanning UK hardware SMEs, major technology companies, venture and public investors, and leading universities., • Shape a growing team with startup pace and close academic links., • Use frontier models and coding agents with a substantial tooling budget., • A collaborative and supportive work environment, • The opportunity to have a high impact in a growing organisation, • Competitive salary package, including pension and benefits, • Professional development opportunities, • Networking opportunities with influential figures from across the tech sector and academia, • A vibrant office environment located a few minutes’ walk away from Cambridge train station Working with us Hybrid, with Cambridge attendance agreed individually around collaboration and the work; applicants across the UK are welcome. We are open to permanent employment, part-time arrangements and consultancy. We would like you to start as soon as practical and can be flexible for the right person. We particularly welcome experienced engineers and can also consider earlier-career candidates with strong evidence of ability. Relevant postgraduate study is useful, but qualifications are secondary to experience and the quality of your work. CommonAI CIC is an equal opportunity employer and is committed to creating an inclusive and diverse workplace. #J-18808-Ljbffr

CommonAI CIC is a non‑profit organisation built around collaborative engineering and is backed by a mix of UK Government and private funding in order to design, build and deploy innovative AI systems. Our Scaling Inference Lab (SIL) is developing simulation grounded in real measurements to help the UK invest in promising AI infrastructure. We connect proposed hardware designs to realistic workloads, helping investors and engineers understand their potential, their limitations and the next experiment worth doing. Build simulation software that helps engineers and investors explore how AI systems would behave with different processors, memory systems, networks or serving strategies. Your work will help turn models and existing software into a practical framework for comparing design choices against realistic LLM workloads. You will develop and connect simulation components, making sure that changes in one part of a system have the appropriate consequences elsewhere. This means understanding unfamiliar code, designing clear interfaces and building software whose predictions can be investigated and tested against measurements. We aim to reuse and extend existing frameworks wherever they provide a sound foundation. We welcome depth in one or more relevant areas, including simulation, systems software, performance modelling and inference serving. Your initial focus will reflect your strengths and the components we choose to build on, with complementary expertise added as the team grows. • Develop and integrate simulation components that capture behaviour and interactions relevant to an AI infrastructure comparison., • Work with the technical lead and our partners to evaluate existing frameworks and software, extending and connecting useful components through clear interfaces., • Develop appropriate models of system state and resource use so that changes in one component have the appropriate consequences elsewhere., • Build reproducible simulation scenarios, diagnostic outputs and tests that expose incorrect assumptions, integration errors and unsupported cases., • Work with measurement and uncertainty colleagues to compare predictions with evidence, improve models and integrate calibration and uncertainty methods into the simulator., • Maintain reliable software, documentation and development workflows, using AI coding agents to support implementation and testing., • An example early project would be to bridge an existing simulator component with our workload models and measurement datasets, producing a reproducible comparison between predicted and measured performance over two systems., • Strong software engineering skills and evidence of building, debugging and maintaining substantial technical software., • Experience in one or more of: discrete‑event simulation; computer architecture or performance modelling; distributed/HPC systems; inference runtimes and serving; memory or network modelling; integration of scientific software and heterogeneous components., • The ability to understand unfamiliar code, reason about interfaces and assess the suitability and maintenance costs of reusing, extending or building software., • A collaborative approach to testing assumptions and working with specialists in adjacent areas., • Practical experience using AI coding agents, with the judgement to check their output for correctness and maintainability. Python, C++, Rust or other relevant systems or scientific programming experience can provide a route into the role. Familiarity with vLLM or SGLang, simulator frameworks, or hardware performance tools would be useful. Depth in a subset of these areas is sufficient; languages and frameworks will follow the engineering needs of the programme. • Help build UK AI capability and improve how investment and compute serve the public good., • Build a network spanning UK hardware SMEs, major technology companies, venture and public investors, and leading universities., • Shape a growing team with startup pace and close academic links., • Use frontier models and coding agents with a substantial tooling budget., • A collaborative and supportive work environment, • The opportunity to have a high impact in a growing organisation, • Competitive salary package, including pension and benefits, • Professional development opportunities, • Networking opportunities with influential figures from across the tech sector and academia, • A vibrant office environment located a few minutes’ walk away from Cambridge train station Hybrid, with Cambridge attendance agreed individually around collaboration and the work; applicants across the UK are welcome. We are open to permanent employment, part‑time arrangements and consultancy. We would like you to start as soon as practical and can be flexible for the right person. We particular welcome experienced engineers and can also consider earlier‑career with strong evidence of ability. Relevant postgraduate study is useful, but qualifications are secondary…?? CommonAI CIC is an equal opportunity employer and is committed to creating an inclusive and diverse workplace. #J-18808-Ljbffr

At WSP, you can access our global scale, contribute to landmark projects and connect with the brightest minds in your field to do the best work of your life. You can embrace your curiosity in a culture that celebrates new ideas and diverse perspectives. You can experience a world of opportunity and the chance to shape a career as unique as you. To find out more about our Building Services Mechanical & Electrical business click on the following link and discover what awaits you at WSP: en-GB/services/mechanical-electrical-and-plumbing • Meeting the requirements of an 'Associate' grade and have a proven track record in both leading projects for your discipline and for an overall project team. Be able to demonstrate where your personal input has led to the commission being won or successfully delivered., • Work in a joint leadership capacity within a mechanical, electrical and public health (MEP) discipline team, providing design and technical advice with a multidisciplinary approach on a wide range of projects., • Able to work using your own initiative with minimal supervision on all technical aspects of their discipline or have specific specialist experience applicable to the role., • Able to work under the clear direction of the client and interpret the needs of the project as a whole to meet the full delivery needs., • Supervise the completion of detailed designs and supervise the work of others in this function., • Consider the feasibility of the project specific to the discipline and lead the overall feasibility study for the project., • Take responsibility for and direct others in the production of detailed and performance specification for the discipline subject to review by colleagues prior to issue., • Reviewing design programmes and ensuring that sufficient resource is available to complete the works, • You will work closely with likeminded individuals on exciting and challenging projects., • Building Services in Cambridge consists of 25 mechanical, electrical, and public health engineers who form part of the National MEP Team of nearly 400 MEP engineers., • We make extraordinary efforts to attract, develop, engage and retain the best professionals in our fields of expertise because this is what makes us great., • We put the highest ethical standards at the centre of all we do, and professionalism is inherent in our offering., • We are humble and act with moral and intellectual integrity, keep our word, treat everyone with respect, support our colleagues, and embrace diversity., • We care about individuals and their progress and offer the most fulfilling career development for our professionals. We promote our young recruits because we believe fresh perspectives bring great ideas and new energy., • You will be based in our Cambridge office, offering easy commuting options via Cambridge Train Station and access into Cambridge City Centre within a 10minute walk., • Having an understanding of the UK and European regulations applicable to the discipline and how to demonstrate this in project execution., • Have UK project experience within Building Services MEP, • Have working understanding of latest electrical design software packages to allow the allocation / direction of work on projects., • Have understanding of working with Revit MEP or other 3D design software to allow the allocation / direction of work on projects., • Have an ability to sketch and communicate technical detail graphically and effectively., • Have working knowledge of AutoCAD to allow the allocation / direction of work on projects., • Low voltage distribution systems, • Low voltage standby generation, • UPS systems, • Cable calculations (via software and by hand), • Lighting calculations (via software and by hand), • Small power, • Fire alarms, • Intruder detection, • Access control & security systems, • Disabled call systems, • Data cabling and outlets, • Lightning protection, • BEng/Bsc/MEng in applicable engineering discipline, • CIBSE / IET / Engineering Council accredited honours degree or specific experience that will lead to Chartership., • Working towards I Eng or C Eng status, • RevitMEP training applied to the discipline Join our close-knit community of talented individuals who share your passion for making a positive impact. Our global team includes more than 83,000 employees, working together to make a difference in communities both close to home and around the world. We are one of the world’s leading engineering and professional services firms. Our passionate people are united by the common purpose of creating positive, long-lasting impacts on the communities we serve through a culture of innovation, integrity, and inclusion. With over 11,000 professionals across the UK and Ireland, we are dedicated to our local communities and propelled by international brainpower. At WSP, we understand that work is just one aspect of your life. It’s important to make time for you, your family, friends, interests and your community. Our hybrid working policy offers the flexibility to work from home two days a week, while also providing opportunities to collaborate in our modern offices across the UK. We welcome applicants with varied backgrounds and experiences. We enable rewarding careers by encouraging people to bring their whole and authentic selves to work so that our work represents the fullest spectrum of society. We celebrate integrity and treat people with respect, supporting each other and embracing a culture of inclusion and belonging at WSP. We have our employee resource groups bring together employees and allies with different backgrounds to promote a culture of inclusivity. We are committed to supporting our people, giving you the tools to make improvements to your health and wellbeing through our Thrive programme. Our Virtual GP service gives you access to an NHS or Irish Medical Council GP at a time and place that suits you – giving you peace of mind and quick access to medical advice when you need it most. We also provide reasonable workplace adjustments for those in need. Additionally, you can benefit from the Gymflex scheme, which offers up to 40% off annual gym memberships through our WSP flexible benefits program, as well as a comprehensive menopause support package. To enhance work-life balance, WSP offers the "WSP My Hour," allowing you to take one hour each day for personal activities, with the flexibility to make up the time earlier or later that day. We also provide part-time and flexible working arrangements, the option to purchase additional leave, and the ability to use your bank holiday entitlement to suit you. We understand the importance of development and training to you. That’s why we foster a supportive environment that invests in your growth, whether through training, mentoring, or Chartership. Here at WSP we positively encourage applications from suitably qualified and eligible candidates regardless of sex, race, disability, age, sexual orientation, gender reassignment, religion or belief, marital status, pregnancy or maternity/paternity. As a Disability Confident leader, we will interview all disabled applicants who meet the essential criteria, please let us know if you require any workplace adjustments in support of your application. #J-18808-Ljbffr

CommonAI CIC is a non-profit organisation built around collaborative engineering and is backed by a mix of UK Government and private funding in order to design, build and deploy innovative AI systems. Our Scaling Inference Lab (SIL) is developing simulation grounded in real measurements to help the UK invest in promising AI infrastructure. We connect proposed hardware designs to realistic workloads, helping investors and engineers understand their potential, their limitations and the next experiment worth doing. Make uncertainty a first-class output of an AI systems simulator. When a simulation predicts that one design will outperform another, you will help establish how much confidence the evidence supports, which assumptions could change the conclusion, and which experiments would reduce the uncertainty that matters to the decision. You will combine mathematical reasoning with practical software engineering to calibrate models against measurements, test predictions on independent cases and build uncertainty analysis into the simulation workflow. Your methods and software will help engineers and investors understand what the simulator predicts and the limits of those predictions. We welcome people from AI systems, HPC and other fields where simulation is tested against experiments. You might bring systems engineering experience and an interest in developing uncertainty methods, or experience in mathematical modelling and scientific computing that you want to apply to AI infrastructure. • Develop and implement methods to calibrate simulator models against measurements and evaluate predictions on cases that were not used for calibration., • Distinguish uncertainty arising from measurements, model assumptions and parameters, operational variation and limited simulation runs, and represent it appropriately., • Analyse sensitivity and uncertainty in comparisons between alternatives, accounting for shared sources of uncertainty and their effects on the comparison., • Develop outputs that show how uncertain a prediction is, when a ranking could change, and when a result extends beyond the evidence used to validate it., • Work with measurement and workload engineers to design experiments that test modelling assumptions and reduce uncertainty that matters to the decision., • Integrate these methods into repeatable simulation workflows and communicate their assumptions and limitations to engineers and the technical analyst. An example early project might be to establish a calibration and validation workflow for the a comparison between two architectures, with uncertainty outputs that help explain the result. You will develop and review the methodology with the technical lead and Scientific Director, SIL. • Mathematical maturity: the ability to reason carefully about models, data, variation and the assumptions behind a quantitative claim., • Software engineering experience sufficient to turn numerical methods or analysis into readable, tested, reusable tools., • Relevant experience in one or more of: quantitative systems performance analysis, statistical modelling, simulation validation, uncertainty quantification, scientific/HPC software or the theory of experimental design., • The ability to collaborate with specialists and explain both a result and the limits of the evidence supporting it., • Practical experience using AI coding agents and checking their output. Experience with model discrepancy, sensitivity analysis, Bayesian methods, surrogate models or sequential experimental design would be useful. So would familiarity with computer architecture or inference systems. We welcome complementary backgrounds: strong systems engineers developing uncertainty expertise, and engineers with modelling or uncertainty expertise moving into AI infrastructure. • Help build UK AI capability and improve how investment and compute serve the public good., • Build a network spanning UK hardware SMEs, major technology companies, venture and public investors, and leading universities., • Shape a growing team with startup pace and close academic links., • Use frontier models and coding agents with a substantial tooling budget., • A collaborative and supportive work environment, • The opportunity to have a high impact in a growing organisation, • Competitive salary package, including pension and benefits, • Professional development opportunities, • Networking opportunities with influential figures from across the tech sector and academia, • A vibrant office environment located a few minutes' walk away from Cambridge train station Hybrid, with Cambridge attendance agreed individually around collaboration and the work; applicants across the UK are welcome. We are open to permanent employment, part-time arrangements and consultancy. We would like you to start as soon as practical and can be flexible for the right person. We particularly welcome experienced engineers and can also consider earlier-career candidates with strong evidence of ability. Relevant postgraduate study is useful, but qualifications are secondary to experience and the quality of your work. CommonAI CIC is an equal opportunity employer and is committed to creating an inclusive and diverse workplace. #J-18808-Ljbffr

Company: Company: Cambridge University Hospitals NHS Foundation Trust Working within the Operations portfolio and reporting to the Head of EPRR, the EPRR Manager provides specialist operational leadership to ensure that the Trust can prepare for, respond to and recover from incidents. Working as part of the EPRR team, the post holder will support the Trusts in delivering their statutory duties as Category 1 Responders under the Civil Contingencies Act 2004, the NHS England EPRR Core Standards, and the Health and Care Act 2022. The role involves the development, implementation, and maintenance of comprehensive incident response and business continuity arrangements, ensuring the Trusts are prepared for and resilient to major incidents, critical incidents, and business continuity disruptions. The post-holder will engage proactively with Divisions, Estates, Security, ICT/Telecoms, Governance & Risk, to develop and test integrated arrangements. Represent the trust at Local Resilience Forum and Local Health Resilience Partnership meetings where appropriate and deputise for the Head of EPRR, promoting multi-agency partnership and advocating for acute health requirements. The post-holder will provide specialist advice to managers and on‑call teams during incidents and exercises, staff the Incident Coordination Centre (ICC) and maintain its readiness; deputise for the Head of EPRR when delegated. Planning, Risk and Assurance Maintain both Trusts’ EPRR and business continuity plans, ensuring annual review and compliance with national guidance. Assess EPRR risks against the National and Community Risk Registers, maintain EPRR risk register entries and escalate through governance. Coordinate designated Core Standards, assessing compliance, compiling evidence and drafting improvement actions for approval by the Head of EPRR. Business Continuity Lead defined areas of the business continuity programme, supporting Divisions to develop, maintain and test their own plans and BIAs. Maintain the central BC repository and coordinate the annual review and test cycle. Provide specialist advice during disruptions, including incident logging and action tracking. Training, Exercising and Debrief Design and deliver EPRR and business continuity training and exercises for staff at all levels, maintaining training records. Coordinate debriefs and prepare lessons identified reports, tracking improvement actions to closure. Maintain the Incident Coordination Centre and its readiness. Support tactical and strategic commanders during incidents, including out of hours when delegated. Partnership and Governance Build relationships with the ICB, LHRP, LRF and partner agencies, deputising for the Head of EPRR when required. Prepare reports for governance forums and draft Board material for the Head of EPRR. Our Trust Cambridge University Hospitals (CUH) NHS Foundation Trust comprises Addenbrooke’s Hospital and the Rosie Hospital in Cambridge. With over 13,000 staff and over 1100 beds the priorities of the Trust focus on a quality service which is all about people – patients, staff and partners. Recognised as providing ‘outstanding’ care to our patients and rated ‘Good’ overall by the Care Quality Commissioner, is testament to the skill and dedication of the people who work here. CUH’s values – Together - Safe, Kind, Excellent – are at the heart of patient care, defining the way all staff work and behave. The Trust provides accessible high-quality healthcare for the local people of Cambridge, together with specialist services, dealing with rare or complex conditions for a regional, national and international population. CUH is committed to promoting a diverse and inclusive community - a place where we can all be ourselves. We value our differences and fully advocate and support an inclusive working environment where every individual can fulfil their potential. We want to ensure our people are truly representative of all the communities that we serve. We welcome applications for all positions in the organisation irrespective of people’s age, disability, ethnicity, race, nationality, gender identity, sex, sexual orientation, religion or belief, marriage and civil partnership status, or pregnancy and maternity status or social economic background. Please see the attached Applicant Information Pack (combined Job Description and Person Specification) for key duties and responsibilities. Please note:- Internal applicants on permanent contracts can apply for this post as a secondment or fixed term contract. For secondments you must have the approval of your line manager before applying. If you are an internal applicant currently on a fixed term contract you are able to apply for this as a fixed term position. This vacancy will close at midnight on 26th October 2026. Interviews are due to be held on 4th November 2026. All appointments with a start date on or after 1 October 2026 will be subject to a 12-week probationary period. This provides a structured and supportive period to help you settle into your role, with regular reviews and feedback. Right to Work As there is a 12-week probationary period, successful candidates are expected to have the Right to Work for a period of at least 6 months from the proposed start date. Immigration and Right to Work status will be discussed with successful candidates at conditional offer. CUH will not offer sponsorship to candidates that have applied for a fee waiver. Benefits to you At Cambridge University Hospitals, we want to do all we can to support good working days. We offer development opportunities and a wide range of benefits, including on-site leisure facilities, shopping concourse and day nurseries. Our good work programme currently includes providing reduced cost Stagecoach bus travel to and from Cambridge University Hospital site. Park and Ride bus journeys between Babraham Road and Trumpington sites are free, as is the route to and from Cambridge train station and our hospitals. We also subsidise the cost of parking on site for eligible staff. On CUH campus, hot food is available 24/7 and at a reduced cost for colleagues. Recently we launched the first of our staff pod break spaces. Located in the Deakin Centre, we have a purpose-created colleague-only café, with free tea and coffee, a break space and private outside area for colleagues to rest, refuel and recharge. Just one of the ways we are working hard to support good working days at CUH. CUH is committed to assisting employees in achieving a good work-life balance irrespective of role or personal circumstances. Flexible arrangements may include, but are not limited to, part-time working, job-share, term-time working and flexible start and finish times. Please note if you would like to discuss the required hours of this role further, you should approach the contact given. In some cases, alternative working hours will be considered. We welcome applications from the Armed Forces. This advert closes on Monday 26 Oct 2026 Cambridge University Hospitals NHS Foundation Trust Cambridge University Hospitals NHS Foundation Trust #J-18808-Ljbffr

CommonAI CIC is a non-profit organisation built around collaborative engineering and is backed by a mix of UK Government and private funding in order to design, build and deploy innovative AI systems. Our Scaling Inference Lab (SIL) is developing simulation grounded in real measurements to help the UK invest in promising AI infrastructure. We connect proposed hardware designs to realistic workloads, helping investors and engineers understand their potential, their limitations and the next experiment worth doing. Design and run experiments that reveal how AI infrastructure behaves, what limits its performance and how reliably that behaviour can be predicted. You will build measurement tools and turn observations of real hardware and software into evidence for SIL's simulation platform and the investment decisions it supports. Your work will range from targeted experiments on individual components to measurements of complete LLM serving systems. Working with workload, simulation and uncertainty specialists, you will investigate bottlenecks, explain performance variation and identify the meaningful effects. We welcome deep expertise in one or more parts of the hardware and software stack. You will work with colleagues who bring complementary strengths, with scope to broaden your expertise as the team grows. • Design and run reproducible experiments, from individual hardware or software components to complete LLM serving systems, covering relevant workloads, configurations and operating conditions., • Develop reusable measurement tools and collect traces, profiles and counters to characterise latency, throughput, memory behaviour and, where reliable measurement is possible, power and energy., • Investigate bottlenecks, performance variation and unexpected behaviour across the layers your expertise covers, working with colleagues to extend the investigation through the stack., • Take responsibility for reproducible workload details, software versions, configuration and measurement methods so results can be reproduced, compared and used to calibrate simulator models., • Work with simulation and uncertainty specialists to choose measurements that resolve important unknowns and undertake curation of calibration and validation data., • Help define the controls needed for experiments and use the resulting cluster-control interfaces, coordinating access and changes with colleagues responsible for the hardware. An example early project would be to work with workload colleagues to establish a reproducible reference experiment and measurement dataset, investigating how performance changes under a selected configuration or load change. • Experience investigating the performance of real computing systems and designing experiments that support rigorous conclusions., • Depth in one or more relevant areas: accelerator profiling, inference runtimes, memory systems, distributed/HPC workloads, networking, or power and energy measurement., • The ability to build maintainable automation and analysis tools in a suitable software language, and to learn unfamiliar profiling tools., • Knowledge and care of experimental controls, repeatability, confounding effects and the limits of a measurement., • Some experience using AI coding agents, alongside sound debugging and review habits. LLM serving experience and familiarity with tools such as Nsight, PyTorch Profiler or system/network profilers would be useful. These are examples, not a requirement to know every tool. Strong expertise in one part of the stack is welcome; we can shape the role and recruit complementary strengths around it. • Help build UK AI capability and improve how investment and compute serve the public good., • Build a network spanning UK hardware SMEs, major technology companies, venture and public investors, and leading universities., • Shape a growing team with startup pace and close academic links., • Use frontier models and coding agents with a substantial tooling budget., • A collaborative and supportive work environment, • The opportunity to have a high impact in a growing organisation, • Competitive salary package, including pension and benefits, • Professional development opportunities, • Networking opportunities with influential figures from across the tech sector and academia, • A vibrant office environment located a few minutes' walk away from Cambridge train station Hybrid, with regular time in Cambridge agreed individually around hardware work and collaboration; applicants across the UK are welcome. We are open to permanent employment, part-time arrangements and consultancy. We would like you to start as soon as practical and can be flexible for the right person. We particularly welcome experienced engineers but will also consider earlier-career candidates with strong evidence of ability. Relevant postgraduate study is useful, but qualifications are secondary to experience and the quality of your work. #J-18808-Ljbffr

About CommonAI CommonAI CIC is a non-profit organisation built around collaborative engineering and is backed by a mix of UK Government and private funding in order to design, build and deploy innovative AI systems. Our Scaling Inference Lab (SIL) is developing simulation grounded in real measurements to help the UK invest in promising AI infrastructure. We connect proposed hardware designs to realistic workloads, helping investors and engineers understand their potential, their limitations and the next experiment worth doing. The Opportunity Build the workloads and benchmarks that reveal how AI infrastructure performs on useful applications. You will develop reproducible LLM serving workloads, connecting application behaviour and requirements to physical measurements and SIL's simulation platform. The initial focus includes long-context inference and agentic workloads. You will investigate how request patterns, context lengths, caching, repeated model calls and interactions with tools shape the demands placed on a system. Your benchmarks will make performance comparisons meaningful by specifying the work being done and the quality and service requirements it must meet. Working with measurement and simulation engineers and our partners, you will help preserve the workload behaviour that matters when moving between real systems and models. Your work will help engineers and investors understand which infrastructure choices suit particular applications, and how conclusions change when the workload changes. What you will do • Build and maintain reproducible LLM serving workloads, serving configurations and benchmark tools, including long-context and agentic workloads, using suitable existing software., • Characterise relevant behaviour such as request arrivals, prompt and output lengths, caching, batching, tool interactions and dependencies between requests., • Define performance measures alongside application-quality and service requirements, so comparisons reflect useful work under stated conditions., • Work with measurement engineers to reproduce workloads on physical systems, and with simulator engineers to develop workload representations that preserve the behaviour and dependencies that matter., • Investigate how changes in workloads and serving configurations affect conclusions, and keep the benchmark suite relevant as inference technology and workloads evolve., • Document datasets, model and software versions, configurations, provenance and permitted use so other engineers can reproduce and extend the work. An example early project would be to build a reproducible agentic workload and work with measurement colleagues to compare its performance across two systems, under agreed application-quality and service requirements. Requirements • Experience building or evaluating ML systems, inference services, performance benchmarks or comparable workload-driven systems., • Strong practical software skills for constructing reliable benchmark tools, analysing results and debugging interactions between applications and infrastructure., • Understanding of LLM inference and an interest in how serving choices affect performance and output quality., • Care in designing reproducible comparisons and interpreting results against stated application requirements., • Practical experience using AI coding agents, with the judgement to validate generated code, data transformations and evaluations. Experience with vLLM, SGLang or similar serving software, long-context workloads, agentic applications, trace analysis or quality evaluation would be valuable. Depth in a subset is sufficient. The initial remit is LLM serving; other forms of inference are possible later extensions. Why join us • Help build UK AI capability and improve how investment and compute serve the public good., • Build a network spanning UK hardware SMEs, major technology companies, venture and public investors, and leading universities., • Shape a growing team with startup pace and close academic links., • Use frontier models and coding agents with a substantial tooling budget. Benefits • A collaborative and supportive work environment, • The opportunity to have a high impact in a growing organisation, • Competitive salary package, including pension and benefits, • Professional development opportunities, • Networking opportunities with influential figures from across the tech sector and academia, • A vibrant office environment located a few minutes’ walk away from Cambridge train station Working with us Hybrid, with Cambridge attendance agreed individually around collaboration and the work; applicants across the UK are welcome. We are open to permanent employment, part-time arrangements and consultancy. We would like you to start as soon as practical and can be flexible for the right person. We particularly welcome experienced engineers and can also consider earlier-career candidates with strong evidence of ability. Relevant postgraduate study is useful, but qualifications are secondary to experience and the quality of your work. CommonAI CIC is an equal opportunity employer and is committed to creating an inclusive and diverse workplace. #J-18808-Ljbffr

CommonAI CIC is a non-profit organisation built around collaborative engineering and is backed by a mix of UK Government and private funding in order to design, build and deploy innovative AI systems. Our Scaling Inference Lab (SIL) is developing simulation grounded in real measurements to help the UK invest in promising AI infrastructure. We connect proposed hardware designs to realistic workloads, helping investors and engineers understand their potential, their limitations and the next experiment worth doing. Lead the engineering team building a simulation platform for AI inference systems. You will bring together realistic workloads, hardware models and physical measurements to predict system performance and make the uncertainty in those predictions explicit. The platform will help engineers and investors assess emerging AI infrastructure, understand design choices, and identify next experiments. You will turn scientific direction into an implementable architecture and delivery plan, help shape recruitment, and develop a team with complementary expertise across software, modelling, measurement and hardware. You will report to the SIL Scientific Director and line manage a group of engineers in the growing SIL team. Working closely with the Director of Engineering, you will provide daily technical leadership and help shape the team as we recruit. Technical judgement and the ability to bring people with you matter more than having held a particular management title in the past. • Own the simulator codebase, working closely with the Scientific Director, SIL on architectural decisions. Translate the programme's scientific goals into an achievable engineering roadmap., • Assess existing simulators, serving software and partner components; decide what to reuse, extend or build, and establish interfaces that make the pieces work together., • Shape recruitment, assess candidates, mentor colleagues and line-manage engineers, building complementary expertise across software, measurement, modelling and hardware., • Ensure simulator predictions are tested against physical measurements and realistic LLM workloads, with uncertainty made explicit to the people interpreting the results., • Establish engineering practices for maintainable software, testing, reproducible experiments and effective use of AI coding agents; review designs and results with the relevant specialists., • Contribute directly to implementation and debugging when engineering coverage is incomplete or a colleague needs support., • An example early project would be to lead a complete study from an investor's question through simulation and measured validation to a useful evidence report, while establishing the team and foundations for subsequent cases., • Substantial industrial engineering experience delivering complex software or hardware/software systems, with clear examples of technical decisions you have owned., • Depth in at least one of systems simulation, computer architecture, distributed or HPC systems, inference serving, or experimental performance engineering. Experience across more than one is especially valuable., • Evidence that you can lead people through difficult technical work, explain trade-offs and develop others. This may come from technical leadership and mentoring as well as formal management., • The judgement to turn high-level scientific direction into independent engineering decisions, and to recognise when wider input is needed., • Practical engineering ability to review code and designs, investigate failures and help colleagues resolve implementation difficulties., • Experience using AI coding agents, with the ability to assess their output and help colleagues use them effectively., • Experience connecting research prototypes to maintained engineering systems, collaborating with universities or technology suppliers, and comparing models with measurements would be valuable. We do not expect you to be the deepest specialist in every part of the programme., • Help build UK AI capability and improve how investment and compute serve the public good., • Build a network spanning UK hardware SMEs, major technology companies, venture and public investors, and leading universities., • Shape a growing team with startup pace and close academic links., • Use frontier models and coding agents with a substantial tooling budget., • A collaborative and supportive work environment, • The opportunity to have a high impact in a growing organisation, • Competitive salary package, including pension and benefits, • Professional development opportunities, • Networking opportunities with influential figures from across the tech sector and academia, • A vibrant office environment located a few minutes’ walk away from Cambridge train station Hybrid working, with Cambridge attendance agreed individually around collaboration and job need. We would like you to start as soon as practical and can be flexible for the right person. We value engineering quality and demonstrated leadership over a particular qualification or previous management title. CommonAI CIC is an equal opportunity employer and is committed to creating an inclusive and diverse workplace. #J-18808-Ljbffr

At lowRISC we believe that open source development can transform the semiconductor industry. To help achieve this vision of production-ready, commercial-grade chips and silicon building blocks, we employ a software team with a wide range of skills. Our software engineers work across the full software stack, from firmware and toolchains that interact directly with hardware to cloud-based infrastructure that tests our products at scale. These include work in areas such as: • RISC-V firmware and driver development., • Software hardening and security tools., • Embedded operating systems (in Rust and C)., • Compilers, code generators, and linters., • Simulators and emulators (e.g. QEMU)., • Electronic design automation (EDA) tools., • Performance and code size analysis., • Cloud-based infrastructure (such as continuous integration pipelines). lowRISC is a non-profit Community Interest Company. With a business model that includes member fees and engineering services, we have shown that it is possible to run world-leading projects collaboratively for everyone's benefit. Originally a spinout from Cambridge University's Computer Lab, our UK team is based in Cambridge and our Switzerland team is based in Zurich. Our Ibex CPU and OpenTitan Root of Trust projects have been taken to production silicon, partnering with world-leading organisations including Google, combining open-source approaches with best-practice chip design methodologies. • Highly proficient in Rust programming; deep understanding of fundamental concepts such as the ownership model, lifetimes, concurrency, parallelism, memory management and procedural macros., • Solid knowledge of the Rust ecosystem, including tools and libraries., • A high standard of written and spoken English., • Experience designing, implementing, and maintaining large-scale or modular Rust codebases., • Expert understanding of unsafe Rust, FFI, and low-level systems programming., • Strong proficiency in C; familiarity with C++ and Python are a plus., • Solid understanding of compiler internals; experience with LLVM is a plus., • Experience with build systems and reproducible tooling (e.g. Bazel, Nix, uv)., • Experience developing for embedded or resource-constrained environments, including real-time systems., • Familiarity with CI/CD for embedded or systems software., • Strong knowledge of computer architecture; some experience with assembly languages (x86, RISC-V); familiarity with hardware design is a plus., • Good knowledge of Linux., • Experience contributing to open source projects., • Higher degree levels and academic research experience are highly valued. Salary dependent upon experience. Benefits include a generous employer pension contribution (up to 12.5% subject to an employee contribution via salary sacrifice of 6%), holiday allowance (33 days, pro rata, including public holidays), private medical insurance, group income protection, life insurance and a 4 week paid sabbatical after 4 years of service. The team is based in Cambridge, UK, amongst the Silicon Fen's ecosystem of high tech start ups. Our office is in the heart of the city and a short walk from the train station. We do not discriminate against any employee or candidate because of race, colour, sex, age, national origin, religion, sexual orientation, gender identity, disability, or any other protected characteristic. Please let us know if you need any adjustments made to the application or selection process. We will be happy to help. #J-18808-Ljbffr

At lowRISC we believe that open source development can transform the semiconductor industry. To help achieve this vision of production-ready, commercial-grade chips and silicon building blocks, we employ a software team with a wide range of skills. Our software engineers work across the full software stack, from firmware and toolchains that interact directly with hardware to cloud-based infrastructure that tests our products at scale. This includes work in areas such as: • RISC-V firmware and driver development., • Software hardening and security tools., • Embedded operating systems (in Rust and C)., • Compilers, code generators, and linters., • Simulators and emulators (e.g. QEMU)., • Electronic design automation (EDA) tools., • Performance and code size analysis., • Cloud-based infrastructure (such as continuous integration pipelines). lowRISC is a non-profit Community Interest Company. With a business model that includes member fees and engineering services, we have shown that it is possible to run world-leading projects collaboratively for everyone’s benefit. Originally a spinout from Cambridge University’s Computer Lab, our UK team is based in Cambridge and our Switzerland team is based in Zurich. Our Ibex CPU and OpenTitan Root of Trust projects have been taken to production silicon, partnering with world-leading organisations including Google, combining open-source approaches with best-practice chip design methodologies. • Solid experience with C or Rust., • Strong algorithmic and problem solving skills., • A degree in Computer Science or equivalent experience., • A high standard of written and spoken English., • Experience leading a project or another major initiative, in which you had to communicate and negotiate requirements with multiple stakeholders., • Strong knowledge of computer architecture; some experience with assembly languages (x86, RISC-V)., • Familiarity with C++ and Python., • Experience with build systems and reproducible tooling (e.g. Bazel, Nix, uv)., • Experience developing for embedded or resource-constrained environments, including real-time systems., • Familiarity with CI/CD for embedded or systems software., • A good understanding of compilers and linkers; experience with LLVM is a plus., • Good knowledge of Linux., • Experience contributing to open source projects., • Higher degree levels and academic research experience are highly valued. Salary dependent upon experience. • generous employer pension contribution (up to 12.5% subject to an employee contribution via salary sacrifice of 6%), • holiday allowance (33 days, pro rata, including public holidays), • private medical insurance, • group income protection, • life insurance, • 4 week paid sabbatical after 4 years of service The team is based in Cambridge, UK, amongst the Silicon Fen’s ecosystem of high tech start ups. Our office is in the heart of the city and a short walk from the train station. We do not discriminate against any employee or candidate because of race, colour, sex, age, national origin, religion, sexual orientation, gender identity, disability, or any other protected characteristic. Please let us know if you need any adjustments made to the application or selection process. We will be happy to help. #J-18808-Ljbffr

At lowRISC we believe that open source development can transform the semiconductor industry. To help achieve this vision of production-ready, commercial-grade chips and silicon building blocks, we employ a software team with a wide range of skills. Our software engineers work across the full software stack, from firmware and toolchains that interact directly with hardware to cloud-based infrastructure that tests our products at scale. This includes work in areas such as: • RISC-V firmware and driver development., • Software hardening and security tools., • Embedded operating systems (in Rust and C)., • Compilers, code generators, and linters., • Simulators and emulators (e.g. QEMU)., • Electronic design automation (EDA) tools., • Performance and code size analysis., • Cloud-based infrastructure (such as continuous integration pipelines). lowRISC is a non-profit Community Interest Company. With a business model that includes member fees and engineering services, we have shown that it is possible to run world-leading projects collaboratively for everyone’s benefit. Originally a spinout from Cambridge University’s Computer Lab, our UK team is based in Cambridge and our Switzerland team is based in Zurich. Our Ibex CPU and OpenTitan Root of Trust projects have been taken to production silicon, partnering with world-leading organisations including Google, combining open-source approaches with best-practice chip design methodologies. • Highly proficient in Rust programming; deep understanding of fundamental concepts such as the ownership model, lifetimes, concurrency, parallelism, memory management and procedural macros., • Solid knowledge of the Rust ecosystem, including tools and libraries., • A high standard of written and spoken English., • Experience designing, implementing, and maintaining large-scale or modular Rust codebases., • Expert understanding of unsafe Rust, FFI, and low-level systems programming., • Strong proficiency in C; familiarity with C++ and Python are a plus., • Solid understanding of compiler internals; experience with LLVM is a plus., • Experience with build systems and reproducible tooling (e.g. Bazel, Nix, uv)., • Experience developing for embedded or resource-constrained environments, including real-time systems., • Familiarity with CI/CD for embedded or systems software., • Strong knowledge of computer architecture; some experience with assembly languages (x86, RISC-V); familiarity with hardware design is a plus., • Good knowledge of Linux., • Experience contributing to open source projects., • Higher degree levels and academic research experience are highly valued. Salary dependent upon experience. Benefits include a generous employer pension contribution (up to 12.5% subject to an employee contribution via salary sacrifice of 6%), holiday allowance (33 days, pro rata, including public holidays), private medical insurance, group income protection, life insurance and a 4 week paid sabbatical after 4 years of service. The team is based in Cambridge, UK, amongst the Silicon Fen’s ecosystem of high tech start ups. Our office is in the heart of the city and a short walk from the train station. We do not discriminate against any employee or candidate because of race, colour, sex, age, national origin, religion, sexual orientation, gender identity, disability, or any other protected characteristic. Please let us know if you need any adjustments made to the application or selection process. We will be happy to help. #J-18808-Ljbffr

About CommonAI CommonAI CIC is a non-profit organisation built around collaborative engineering and is backed by a mix of UK Government and private funding in order to design, build and deploy innovative AI systems. Our Scaling Inference Lab (SIL) is developing simulation grounded in real measurements to help the UK invest in promising AI infrastructure. We connect proposed hardware designs to realistic workloads, helping investors and engineers understand their potential, their limitations and the next experiment worth doing. The Opportunity Build simulation software that helps engineers and investors explore how AI systems would behave with different processors, memory systems, networks or serving strategies. Your work will help turn models and existing software into a practical framework for comparing design choices against realistic LLM workloads. You will develop and connect simulation components, making sure that changes in one part of a system have the appropriate consequences elsewhere. This means understanding unfamiliar code, designing clear interfaces and building software whose predictions can be investigated and tested against measurements. We aim to reuse and extend existing frameworks wherever they provide a sound foundation. We welcome depth in one or more relevant areas, including simulation, systems software, performance modelling and inference serving. Your initial focus will reflect your strengths and the components we choose to build on, with complementary expertise added as the team grows. What you will do • Develop and integrate simulation components that capture behaviour and interactions relevant to an AI infrastructure comparison., • Work with the technical lead and our partners to evaluate existing frameworks and software, extending and connecting useful components through clear interfaces., • Develop appropriate models of system state and resource use so that changes in one component have the appropriate consequences elsewhere., • Build reproducible simulation scenarios, diagnostic outputs and tests that expose incorrect assumptions, integration errors and unsupported cases., • Work with measurement and uncertainty colleagues to compare predictions with evidence, improve models and integrate calibration and uncertainty methods into the simulator., • Maintain reliable software, documentation and development workflows, using AI coding agents to support implementation and testing. An example early project would be to bridge an existing simulator component with our workload models and measurement datasets, producing a reproducible comparison between predicted and measured performance over two systems. • Strong software engineering skills and evidence of building, debugging and maintaining substantial technical software., • Experience in one or more of: discrete-event simulation; computer architecture or performance modelling; distributed/HPC systems; inference runtimes and serving; memory or network modelling; integration of scientific software and heterogeneous components., • The ability to understand unfamiliar code, reason about interfaces and assess the suitability and maintenance costs of reusing, extending or building software., • A collaborative approach to testing assumptions and working with specialists in adjacent areas., • Practical experience using AI coding agents, with the judgement to check their output for correctness and maintainability. Python, C++, Rust or other relevant systems or scientific programming experience can provide a route into the role. Familiarity with vLLM or SGLang, simulator frameworks, or hardware performance tools would be useful. Depth in a subset of these areas is sufficient; languages and frameworks will follow the engineering needs of the programme. Why join us • Help build UK AI capability and improve how investment and compute serve the public good., • Build a network spanning UK hardware SMEs, major technology companies, venture and public investors, and leading universities., • Shape a growing team with startup pace and close academic links., • Use frontier models and coding agents with a substantial tooling budget., • A collaborative and supportive work environment, • The opportunity to have a high impact in a growing organisation, • Competitive salary package, including pension and benefits, • Professional development opportunities, • Networking opportunities with influential figures from across the tech sector and academia, • A vibrant office environment located a few minutes’ walk away from Cambridge train station Working with us Hybrid, with Cambridge attendance agreed individually around collaboration and the work; applicants across the UK are welcome. We are open to permanent employment, part-time arrangements and consultancy. We would like you to start as soon as practical and can be flexible for the right person. We particularly welcome experienced engineers and can also consider earlier-career candidates with strong evidence of ability. Relevant postgraduate study is useful, but qualifications are secondary to experience and the quality of your work. CommonAI CIC is an equal opportunity employer and is committed to creating an inclusive and diverse workplace. #J-18808-Ljbffr