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This job is with AstraZeneca, an inclusive employer and a member of myGwork – the largest global platform for the LGBTQ+ business community. Please do not contact the recruiter directly. Job Title: Senior Director, Frontier Science - Early Infectious Disease Location: Cambridge Salary: Competitive Introduction to role: Are you ready to shape how the world sees cutting-edge infectious disease science and turn breakthrough ideas into tangible patient impact? This role is your opportunity to define the external scientific identity of a high-ambition portfolio that spans from target selection through clinical development, connecting bold discovery with real-world outcomes. You will lead three high-impact mandates that amplify our scientific voice and accelerate global health outcomes: build and execute a publication strategy that positions our scientists as leaders; drive partnerships and funding that bring our technologies to areas of greatest need; and keep the organization on the frontiers of emerging science and technology. Can you envision yourself crafting narratives that energize the field, securing grants that save lives, and spotting the next wave of innovations that close infectious-disease gaps? In this seat, you will partner with discovery and clinical leaders, data and technology experts, and external collaborators across academia, biotech's and funders. You will foster an innovative culture, influence senior stakeholders, and guide teams through uncertainty, ensuring that our science is communicated with rigor, ambition and clarity. Accountabilities: • Publication Strategy Leadership: Define and lead the end-to-end publication strategy for the portfolio, mapping each asset's scientific story from inception through clinical development and up to patenting, with clear milestones and decision points., • Scientific Narrative and Communications: Shape compelling narratives and oversee high impact abstracts, posters and manuscripts for leading journals and major congresses, ensuring scientific rigor, timely delivery and measurable reach., • Thought Leadership and External Positioning: Drive external scientific communication that builds excitement around our products and establishes our scientists as recognized infectious-disease leaders, anchored in transformative evidence., • Global Health Strategy and Partnerships: Lead strategy to align infectious-disease efforts with global health targets; cultivate relationships with NGOs, venture partners and philanthropic funders to translate our technologies into real-world solutions., • Funding and Grants: Systematically identify funding opportunities; write and secure endorsed grants that bring our platforms to areas of highest unmet need; set success criteria and track outcomes., • Program Delivery and Governance: Manage a portfolio of externally funded and global health projects with the mission of saving lives, ensuring robust governance, risk management, compliance and impact reporting., • Horizon Scanning and Technology Foresight: Lead continuous evaluation of emerging biotechnology and start-up science, generating early cases for next-generation technologies critical to infectious disease challenges., • Innovation Ecosystem Engagement: Maintain a special line of sight to leading countries and academic and innovation hubs; build collaborations that accelerate access to novel modalities, platforms and data capabilities., • Stakeholder Influence and Collaboration: Influence senior internal and external stakeholders to align on strategy, trade-offs and investments; communicate results, risks, uncertainties and limitations clearly and objectively., • Culture and Team Leadership: Foster an innovative, learning-oriented culture; coach teams to move with pace, think differently and integrate cross-functional expertise to maximize success. Essential Skills/Experience: • PhD or MD or equivalent in infectious disease, immunology, microbiology, virology, marine biology, biochemistry, molecular biology or a related discipline, with extensive industry and/or academic experience, • Experience as an editor, writer, or in publication strategy, • Proven track record in building scientific narratives and editing abstracts, posters and manuscripts in peer-reviewed journals and at major congresses, • Demonstrated experience leading scientific communication or publication strategy across the discovery-to-clinical-development continuum, • Experience building and managing relationships with external partners such as NGOs, funders, venture partners or academic centers, • Experience in identifying funding, write and secure grants, and manage externally funded or global health projects, • Strong awareness of emerging biotechnology and ability to evaluate early-stage science and technology, • Experience working in cross-functional teams to maximize expertise and accelerate success, • Excellent written and verbal communication, business analysis, and stakeholder-influencing skills Desirable Skills/Experience: • PhD or MD with a recognized scientific reputation in infectious disease, immunology or vaccines, • Established network among journal editors, key opinion leaders, NGOs and global health funders, • Familiarity with the global innovation ecosystem and other leading global academic and biotech hubs, • Experience in global health, public-private partnerships, or non-dilutive and philanthropic funding, • Experience in technology scouting or search and evaluation, • Proven track record of writing/editing/publishing relevant results and tools in peer-reviewed journals, conferences, and other scientific proceedings, • Experience operating in a complex global organization across multiple countries and cultures When we put unexpected teams in the same room, we unleash bold thinking with the power to inspire life-changing medicines. In-person working gives us the platform we need to connect, work at pace and challenge perceptions. That's why we work, on average, a minimum of three days per week from the office. But that doesn't mean we're not flexible. We balance the expectation of being in the office while respecting individual flexibility. Join us in our unique and ambitious world. Why AstraZeneca: Here you will work at the frontiers of science, data and technology to tackle some of the most complex diseases on the planet, with the support and trust to think differently and the resources to move fast. We bring diverse experts together immunologists, microbiologists, data scientists and clinicians around the same whiteboard to turn uncertainty into breakthroughs and to shape therapies with the potential to reach patients at scale. You will grow through lifelong learning, stretch assignments and the chance to publish when transformative science comes alive, all within a culture that values kindness alongside ambition and celebrates bold decisions grounded in biology and evidence. What is next? If you are ready to define the scientific voice of a high-impact infectious disease portfolio and convert bold ideas into measurable global health outcomes, send us your resume and step into the future you want to lead. We look forward to receiving your application not later than 10th November 2026 Salary: Negotiable

Job DescriptionAbout AH Technology Solutions:Animal health Technology Solutions offers global animal identification and monitoring solutions for companion animals and livestock. Scroll down the page to see all associated job requirements, and any responsibilities successful candidates can expect. These solutions enable pet owners to better manage the health and well-being of their pets and enable livestock producers to improve well-being and animal production. Technology Solutions include Core Technologies in Livestock Identification, Dairy, Beef, and Swine monitoring, for which the product/market fit is established, as well as products in discovery.Our Technology Solutions division is evolving. We are moving to a Product Operating Model – a new way of working that empowers product teams to take full ownership and accountability for delivering customer and business value in ways our customers love and that work for the business.Our product and platform teams are cross-functional and durable, bringing together all the skills required to deliver impactful outcomes. Each team includes a Product Manager, Technology Lead, Product Designer, Delivery Lead, engineers, and developers.We believe in customer centricity, ownership, and accountability. Solutions must be:· Valuable – customers choose to buy or use them· Viable – aligned with business constraints and goals· Usable – intuitive and user-friendly· Feasible – achievable with our technical capabilitiesLeadership of the Product team is shared between the Product Manager and the Technology Lead, ensuring balanced guidance that bridges product vision and technical execution.Role summary:As Senior System Engineer you will have significant impact on our multi-disciplinary R&D projects, grow with us as we venture into new technologies and will lead work groups in our projects as part of the connected devices line of products during the product lifecycle of both discovery and delivery.The responsibilities will include defining System, HW, Embedded, ATE, SW & Integration requirements as well as contributing to the design stage of each discipline, defining the protocol, technical problem solving, proof of concept and field testing and assisting in tests design and investigations.The position requires multidisciplinary understanding, ability to quickly grasp new technological aspects as well as constraints , the capability to work in a multi-site matrix organization and working closely in a Product and Platform team with other system engineers, embedded software developers, HW engineers, Integration Engineers as well as backend and front-end teams. Requirements:· BSC/MSc in Electrical engineering· Minimum of 5 years of experience in system design of multidisciplinary systems· Experience in working with communication products, Embedded, HW, RF systems· Problem solver with good personal and teamwork skills.· Ability to understand complex IoT systems and quickly grasp new technical concepts with focus on challenges ahead and alternative comparison· Experience of working in complex multidisciplinary projects that requires a high level of technical know-howAdvantage:· Experience with Linux environment· Experience in working with Atlassian and Azure DevOps· Experience in working with Agentic AI for discovery and innovation· Coding capabilities (C/C#/Java/Python)· Experience with RF communication technologies (BLE, WiFi, LTE-M, LoRa, etc)· Knowledge in backend systems and serverless solutions· Working with Cloud based infrastructure for IoT devicesCharacteristics:· Ability to focus on what's important.· Organized and able to work well in a structured process environment· Strong desire for learning and innovation.· “hands-on” approach.· Excellent team player.· Good verbal & written communication Skills.MDAHTLAdditional location(s) Compensation Information:Required Skills: Cloud Infrastructure, Embedded Software, Internet of Things (IoT), Product Lifecycle, Python (Programming Language), Systems Engineering, Written CommunicationPreferred Skills: Database Design, Emerging Technologies, Hardware Design, Management System Development, Network Design, Radio Frequency Engineering, Real-Time Programming, Requirements Management, Software Development, Software Development Life Cycle (SDLC), Solution Architecture, System Designs, System Integration, Technical Advice, TestingCurrent Employees apply HERECurrent Contingent Workers apply HERESearch Firm Representatives Please Read Carefully Merck & Co., Inc., Rahway, NJ, USA, also known as Merck Sharp & Dohme LLC, Rahway, NJ, USA, does not accept unsolicited assistance from search firms for employment opportunities. All CVs / resumes submitted by search firms to any employee at our company without a valid written search agreement in place for this position will be deemed the sole property of our company. No fee will be paid in the event a candidate is hired by our company as a result of an agency referral where no pre-existing agreement is in place. Where agency agreements are in place, introductions are position specific. Please, no phone calls or emails. Employee Status: RegularRelocation:VISA Sponsorship:Travel Requirements:Flexible Work Arrangements:HybridShift:Valid Driving License:Hazardous Material(s):Job Posting End Date:11/8/2026 *A job posting is effective until 11:59:59PM on the day BEFORE the listed job posting end date. xrnqpay Please ensure you apply to a job posting no later than the day BEFORE the job posting end date. SummaryLocation: ISR • North Central District, • Netanya; GBR, • Cambridgeshire, • Cambridge (ANTELLIQ)Type: Full time

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

PE Global is currently recruiting for an Embedded Firmware Engineer to join our technology client based in Cambourne. The role is an initial 24 month contract, working on a hybrid basis with a minimum of 2 days per week onsite. As an Embedded Software Engineer, you will have significant impact on our multidisciplinary R&D projects, grow with us as we venture into new technologies, and contribute to the connected devices line of products throughout both discovery and delivery. You will design, develop, debug, and optimize robust, production-ready firmware for resource-constrained, low-power IoT devices. The responsibilities will include designing, developing, debugging, and optimizing embedded firmware for low-power STM32-based IoT devices; integrating GNSS, LoRa or LoRaWAN, and hardware peripherals; Developing within an RTOS environment; and delivering reliable, maintainable, production-ready code in collaboration with multidisciplinary product and platform teams in the UK, Israel and California, USA. • Approximately five years of embedded software engineering experience using C/C++., • Hands-on experience with STM32 microcontrollers and embedded debugging., • Proven delivery of low-power firmware for battery-operated devices., • Experience with GNSS, LoRa or LoRaWAN, embedded peripherals, and an RTOS., • Ability to work independently and deliver production-ready, maintainable firmware., • Experience with u-blox M10 or newer GNSS architectures., • Familiarity with oscilloscopes, logic analyzers, multimeters, and J-Link or ST-Link., • Experience with Git and collaborative development workflows., • Ability to read hardware schematics and PCB layouts., • Experience using AI-assisted development tools such as GitHub Copilot or comparable tools. Please note PE Global cannot provide visa sponsorship for this role, candidates will need the full right to live and work within the UK for at least the next 24 months. Although it is not possible for us to respond to all applications, we at PE Global will do our upmost to give you feedback on your application. You have sent your Cv into us as a company and even though you have sent your CV to a particular position, we are making the reasonable assumption that you are active on the job market and as part of our normal recruitment service we will discuss other suitable positions with you. You are free to opt out of this so please specify in your application to us if you just want to be contacted in relation to a specific vacancy. Your Cv is sent to a central recruitment inbox which a number of people in the applicable PE Global division have access to and so this means that you might not be contacted by the named person in this advert. #J-18808-Ljbffr

Senior Automation/Test Development Engineer Location: Cambridge Type: Full-Time (on-site) Coherence Neuro is a fast-growing, venture-backed company pioneering implantable technologies that interface with the human body to address critical unmet medical needs. We're looking for driven individuals who are passionate about pushing the boundaries of engineering and science to solve some of the most complex biological challenges. In this early-stage environment, we seek versatile team members with a hands‑on approach and a willingness to learn quickly and adapt. We value diversity and encourage applicants from all backgrounds to join us in our mission. Learn about our team here! We are seeking a Senior Automation/Test Development Engineer to lead the development of custom test systems supporting the SOMA program at our Cambridge office. This role will lead the architecture, hardware, software, and documentation of scalable test platforms supporting engineering characterisation, design verification, and future manufacturing needs. The successful candidate will be a hands‑on engineer capable of building reliable automated systems, mentoring junior staff, and ensuring that test equipment and methods can be reproduced, controlled, and used to support regulatory submissions. • Lead the design, development, and deployment of custom automated test systems., • Translate technical requirements into test methods, fixtures, instrumentation, software, and data outputs., • Design and integrate custom electronics, PCBs, fixtures, sensors, cabling, and commercial laboratory equipment., • Develop software for instrument control, test sequencing, data acquisition, analysis, and reporting., • Improve test repeatability, throughput, traceability, and scalability, including parallel testing where appropriate., • Define and verify tester accuracy, calibration, measurement uncertainty, and system performance., • Produce the procedures, drawings, schematics, bills of materials, software records, and verification documentation needed to rebuild and operate systems repeatably., • Work across engineering, quality, and preclinical teams to develop technically meaningful and regulatory-relevant test methods., • Troubleshoot complex hardware, software, instrumentation, and measurement issues., • Mentor junior engineers and support the transition of test systems into controlled verification and manufacturing methods., • Bachelor’s degree in Electrical Engineering, Electronic Engineering, Biomedical Engineering, Physics, or a related discipline., • 4+ years of experience developing automated test systems, electrical test equipment, laboratory instrumentation, or product characterisation platforms., • Experience independently designing and commissioning custom test hardware., • Experience automating laboratory equipment using Python, LabVIEW, or a comparable environment., • Experience with PCB design, interface electronics, fixtures, sensors, and cable assemblies., • Strong hands‑on troubleshooting skills across hardware, software, instrumentation, and devices under test., • Understanding of measurement accuracy, repeatability, calibration, and uncertainty., • Ability to turn loosely defined engineering questions into practical and reliable test methods., • Strong technical documentation, communication, and mentoring skills., • Experience developing test systems for medical devices, implantable electronics, or other regulated products., • Experience with electrode characterisation, electrochemical impedance spectroscopy, electrical stimulation, or bioelectronic interfaces., • Experience with wireless power, RF, inductive systems, or power‑conversion testing., • Familiarity with test‑method validation, equipment qualification, design verification, or ISO 13485., • Familiarity with IEC 60601, ISO 14708, or related medical‑device standards., • Experience scaling engineering test systems toward production use., • Competitive salary and share options, • Matched pension, • on-site daily meals + snacks, • 25 days PTO + England bank holidays, • Professional development support and training opportunities, • Flexible working hours and travel opportunities (We have sites in Australia, UK and US!), • A chance to work on impactful, life‑changing technologies in a dynamic, innovative environment. #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. 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

We are looking for a Senior GNC Engineer to own navigation for a new interceptor from early concept through sensor integration, real-time implementation, and flight test. You will define the navigation architecture and develop robust solutions for contested environments. Your work will influence systems design and interceptor performance across demanding flight regimes. • Shape the interceptor concept through navigation trade studies influencing system architecture, sensor selection and overall interceptor performance, • Own navigation architecture and algorithm development from concept through flight test, • Characterize sensor error models and implement calibration, alignment and fault‑detection logic, • Develop and tune state-estimation and sensor-fusion algorithms for guidance and control in high-dynamic flight, • Plan and support navigation simulation campaigns, ground tests, and flight trials, • GNC experience in missiles, rockets or high-speed aircraft, including evidence of technical ownership for a GNC workstream, • Deep understanding of inertial navigation, observability, sensor error modelling, alignment and calibration, • Experience developing navigation solutions for degraded, spoofed or GNSS-denied environments, • Experience taking GNC work from concept into integration and test incorporating design assurance processes, • Strong MATLAB/Simulink skills and working knowledge of Python, C/C++, or similar, • Experience in fast-paced R&D or startup environments, with rapid prototype iteration times, • Competitive compensation and stock options, • Private Medical & Dental, • Cycle to work scheme The mission of Cambridge Aerospace is to eliminate the risk of existential war in Europe. That risk comes from the air – low-cost drones and cruise missiles now manufactured in colossal volumes by our adversaries, ballistic missiles capable of ending the world as we know it, and a new generation of hypersonic weapons that evade every existing defence. We’re building low-cost interceptors that defeat these threats, and the surveillance systems to detect and target them – at a cost taxpayers can afford, at the pace the threat demands, and with sovereign supply chains that assure delivery. We have immense backing from the world’s best venture funds. We’re one of the fastest growing defence technology companies in history. And we’re hiring the best engineers to prosecute the mission. If you are in the top 1%, willing to work harder than you ever have, build technology better than anyone else, and contribute to the defining challenge of our time, now is the time to join us. We’re committed to building a diverse and inclusive workplace. If you’re excited about this role but don’t meet every requirement, we still encourage you to apply. #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 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

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

Nuclera is a venture-backed, founder-driven biotech company headquartered in Cambridge, UK with significant operations in Boston, USA. Our purpose is to improve human health by making proteins accessible to life science researchers around the world. In 2024, we launched eProtein Discovery™ - a breakthrough protein expression, characterization, and purification platform, consisting of cloud-based software and a lab-based instrument (with proprietary cartridges and consumables). Since then, we have established a growing installed base in North America, Europe and Asia, receiving numerous awards and customer accolades. As a key player in a small engineering team, you will support the next stage of Nuclera’s growth through: • New Products - Contributing to development of a new product platform and its support tools., • Installed Base Support - Sustaining and being the internal expert and primary point of contact for manufacturing and field support issues relating to current generation Instrument. Over time (6-12 months), your skills and responsibilities will broaden to include other software languages and technologies. This may also extend to electronics and PCB design. • Full lifecycle development and release management for prototypes, products and support tools., • Planning: Providing plans covering tasks, dependencies, effort, cost and resource allocation., • Risk Management: Supporting risk identification, evaluation and choice of risk response strategies., • Execution & Self-Monitoring: Progress according to agreed priorities and plans. Devise appropriate responses to problems and issues. Track and report own progress (deliverables, adherence to schedules, actual spend vs budget, etc.), • Progress according to agreed priorities and plans. Devise appropriate responses to problems and issues. Track and report own progress (deliverables, adherence to schedules, actual spend vs budget, etc.), • Bachelors’ Degree in Software Engineering, Computer Science or similar., • 5+ years of full lifecycle development for multi-technology products developed to rigorous standards. Life science equipment / medical device equipment experience desired., • Programming Languages: C for STM32 and Raspberry Pi Pico 2, python 3, Bash, • Operating Systems / Frameworks: Embedded Linux / Yocto, Docker, • Development Environment: Git / Gitlab / CI/CD, Jira, • Software Quality: coding standards/static analysis, code/design review, testing - unit, integration, hardware-in-the-loop, • Technologies: real-time control & monitoring (motion / mechanism functions & temperature control) embedded systems / electronics, LAN networking, • Strong initiative, problem-solving, task prioritization, and end-to-end execution / completion., • Effective relationship management, cross-functional teamwork, clear documentation, and presentation skills., • Current UK driving licence. Willing and able to occasionally travel nationally / internationally on business as required., • Programming Languages: C for SAM 4S / SAM D20, Java, Javascript / React, • Operating Systems / Frameworks: Torizon, PyQt 6, RTOS, • Development Environment: VS Code, Claude AI, • Software Quality: Software Architecture / UML, Design Controls, Stage-Gate Development, Problem solving (DMAIC, 8D), • Technologies: Microfluidics, Optics / Image Processing, Machine Learning / Neural Networks, Cybersecurity, PCB Design (Altium), GUI Design In addition to competitive salaries, we offer a range of benefits including: • Company bonus scheme of 5%, • Share option incentive scheme, • Life insurance, • Private medical insurance and cash plan, • 25 days' annual leave + Bank Holidays, • Enhanced employer's pension contributions, • Enhanced maternity and paternity Leave, • Investment in professional development and learning, • Fresh fruit, tea, coffee, and snacks in the office, • Organised summer events for staff #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 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. 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. 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. #J-18808-Ljbffr

Nuclera is a venture-backed, founder-driven biotech company headquartered in Cambridge, UK with significant operations in Boston, USA. Our purpose is to improve human health by making proteins accessible to life science researchers around the world. In 2024, we launched eProtein Discovery™ - a breakthrough protein expression, characterization, and purification platform, consisting of cloud-based software and a lab-based instrument (with proprietary cartridges and consumables). Since then, we have established a growing installed base in North America, Europe and Asia, receiving numerous awards and customer accolades. We are looking for an experienced full-stack software engineer to work on our customer-facing cloud and instrument software as well as the internal tools that support our in-house protein screening services. You’ll be involved in the full software development lifecycle, including working closely with biologists and other stakeholders to define use-cases and requirements, translating those requirements into technical designs, implementing those designs in code and writing tests to verify its correctness. No prior knowledge of biology is necessary, but curiosity and an eagerness to learn is critical. About the role This role is for you if you: • Have at least 5 years industry experience as a software engineer., • Are confident working across backend, frontend, dev-ops and QA., • Have a product mindset, with a track record of gathering requirements, owning features end to end, and working directly with key stakeholders to deliver the right outcome., • Are ready to take ownership of code and keep it up-to-date with the latest best-practices. Essential: • Java and Python, • REST and Websocket APIs, • A backend web framework (e.g. Micronaut, Spring), • Google Cloud or an equivalent cloud service provider (e.g. AWS, Azure), • HTML, CSS and TypeScript or JavaScript, • React or an equivalent frontend technology (e.g. Angular), • Containerisation (e.g. Docker, Podman), • Automated testing (e.g. JUnit, Mockito, pytest), • Automated build and deployment pipelines, • Version control in Git Desirable: • Java Reactive Streams, • Asynchronous Python, • PostgreSQL, • Kubernetes, Helm, Flux, Terraform, • Figma, Balsamiq, • GitLab CI, • Claude Code or an equivalent AI coding assistant, • Experience with bioinformatics data processing pipelines What we offer: In addition to competitive salaries, we offer a range of benefits including: • Company bonus scheme of 5%, • Share option incentive scheme, • Life insurance, • Private medical insurance and cash plan, • 25 days' annual leave + Bank Holidays, • Enhanced employer's pension contributions, • Enhanced maternity and paternity Leave, • Investment in professional development and learning, • Fresh fruit, tea, coffee, and snacks in the office, • Organised summer events for staff #J-18808-Ljbffr

Nuclera is a venture-backed, founder-driven biotech company headquartered in Cambridge, UK with significant operations in Boston, USA. Our purpose is to improve human health by making proteins accessible to life science researchers around the world. In 2024, we launched eProtein Discovery - a breakthrough protein expression, characterization, and purification platform, consisting of cloud-based software and a lab-based instrument (with proprietary cartridges and consumables). Since then, we have established a growing installed base in North America, Europe and Asia, receiving numerous awards and customer accolades. We are looking for an experienced full-stack software engineer to work on our customer-facing cloud and instrument software as well as the internal tools that support our in-house protein screening services. You’ll be involved in the full software development lifecycle, including working closely with biologists and other stakeholders to define use-cases and requirements, translating those requirements into technical designs, implementing those designs in code and writing tests to verify its correctness. No prior knowledge of biology is necessary, but curiosity and an eagerness to learn is critical. This role is for you if you: • Have at least 5 years industry experience as a software engineer., • Are confident working across backend, frontend, dev-ops and QA., • Have a product mindset, with a track record of gathering requirements, owning features end to end, and working directly with key stakeholders to deliver the right outcome., • Are ready to take ownership of code and keep it up-to-date with the latest best-practices., • Java and Python, • REST and Websocket APIs, • A backend web framework (e.g. Micronaut, Spring), • Google Cloud or an equivalent cloud service provider (e.g. AWS, Azure), • HTML, CSS and TypeScript or JavaScript, • React or an equivalent frontend technology (e.g. Angular), • Containerisation (e.g. Docker, Podman), • Automated testing (e.g. JUnit, Mockito, pytest), • Automated build and deployment pipelines, • Version control in Git, • Java Reactive Streams, • Asynchronous Python, • PostgreSQL, • Kubernetes, Helm, Flux, Terraform, • Figma, Balsamiq, • GitLab CI, • Claude Code or an equivalent AI coding assistant, • Experience with bioinformatics data processing pipelines In addition to competitive salaries, we offer a range of benefits including: • Company bonus scheme of 5%, • Share option incentive scheme, • Life insurance, • Private medical insurance and cash plan, • 25 days' annual leave + Bank Holidays, • Enhanced employer's pension contributions, • Enhanced maternity and paternity Leave, • Investment in professional development and learning, • Fresh fruit, tea, coffee, and snacks in the office, • Organised summer events for staff #J-18808-Ljbffr

Job Description Summary#LI-HybridLocation: Cambridge, BostonRelocation Support: This role is based in Cambridge, Boston. Novartis is unable to offer relocation support: please only apply if accessible.Sponsorship: Novartis is unable to offer sponsorship for this role.Turn scientific insight into investment decisions that could help shape the future of medicine. As an Analyst with the Novartis Venture Fund, you'll join a small, highly collaborative team investing in innovative life sciences companies developing potentially paradigm-changing therapies in areas of high unmet need. You'll work alongside experienced venture investors across scientific and business due diligence, market and competitive analysis, investment evaluation and portfolio support, while building your understanding of the wider drug development and investment landscape. With exposure to senior decision-makers, entrepreneurs, company leaders and scientific experts, this is an opportunity to build on your scientific expertise, broaden your commercial perspective and develop as a life sciences venture investor.Job DescriptionKey Responsibilities· Support scientific and business due diligence for new and follow-on life sciences investments.· Evaluate scientific publications, business plans and emerging technologies to inform investment decisions.· Conduct competitive, market and landscape analyses across therapeutic areas and emerging science.· Engage with scientific experts and key opinion leaders to test investment hypotheses.· Assess companies, development programs and market opportunities alongside senior investment colleagues.· Build financial and capital models to support investment evaluation and decision-making.· Prepare clear investment memoranda, financial analyses and presentations for internal review.· Coordinate deal flow and maintain accurate documentation across prospective and existing investments.· Support portfolio companies by coordinating interactions across the Novartis Venture Fund and wider Novartis organization.· Collaborate with senior colleagues and external stakeholders across the life sciences and venture capital ecosystem.Essential Requirements· Doctor of Philosophy and/or Doctor of Medicine in natural sciences, medicine or a closely related discipline.· At least two years' experience within the pharmaceutical, biotechnology or investment industry.· Strong scientific expertise with an understanding of life sciences, drug development, medical needs and emerging scientific trends.· Experience with, or strong understanding of, scientific due diligence and evaluating scientific evidence.· Strong analytical and conceptual skills, with the ability to connect scientific insight with broader business and market considerations.· Excellent written and verbal communication skills, including confidence engaging with senior stakeholders and external experts.· Ability to prioritize and manage multiple projects effectively in an international, high-performance environment.· Proactive, collaborative and intellectually curious approach, with a strong interest in research, analysis and life sciences investing.Desirable Requirements· Master of Business Administration or equivalent business education would be an advantage.· Additional language capabilities beyond fluent English would be beneficial.Why Novartis?At Novartis, our purpose is to reimagine medicine to improve and extend people’s lives. As a Novartis Venture Fund Analyst, you will have the opportunity to help evaluate breakthrough science and emerging life sciences companies with the potential to address significant unmet medical needs. Working alongside experienced venture investors, entrepreneurs, scientific experts, and company leaders, you will build your investment capabilities while bringing rigorous scientific thinking to decisions that can help shape the future of medicine.Benefits & RewardsThe salary for this position is expected to range between $132,300 - $245,700 USD per year.The final salary offered is determined based on factors like, but not limited to, relevant skills and experience, and upon joining Novartis will be reviewed periodically. Novartis may change the published salary range based on company and market factors.Your compensation will include a performance-based cash incentive and, depending on the level of the role, eligibility to be considered for annual equity awards.US-based eligible employees will receive a comprehensive benefits package that includes health, life and disability benefits, a 401(k) with company contribution and match, and a variety of other benefits. In addition, employees are eligible for a generous time off package including vacation, personal days, holidays and other leaves.To learn more about the culture, rewards and benefits we offer our people, click here.Commitment to Diversity and InclusionNovartis is committed to building an outstanding, inclusive work environment and diverse teams representative of the patients and communities we serve.The Novartis Group of Companies are Equal Opportunity Employers and take pride in maintaining a diverse environment. We do not discriminate in recruitment, hiring, training, promotion or other employment practices for reasons of race, color, religion, gender, national origin, age, sexual orientation, gender identity or expression, marital or veteran status, disability, or any other legally protected status. We are committed to building diverse teams, representative of the patients and communities we serve, and we strive to create an inclusive workplace that cultivates bold innovation through collaboration and empowers our people to unleash their full potential.Accessibility and AccommodationThe Novartis Group of Companies are committed to working with and providing reasonable accommodation to individuals with disabilities.If, because of a medical condition or disability, you need a reasonable accommodation for any part of the application process, or in order to perform the essential functions of a position, please send an email to us.reasonableaccommodations@novartis.com or call +1 (877) 395-2339 and let us know the nature of your request and your contact information. Please include the job requisition number in your message.EEO Statement:The Novartis Group of Companies are Equal Opportunity Employers. We do not discriminate in recruitment, hiring, training, promotion or other employment practices for reasons of race, color, religion, sex, national origin, age, sexual orientation, gender identity or expression, marital or veteran status, disability, or any other legally protected status. Accessibility and reasonable accommodationsThe Novartis Group of Companies are committed to working with and providing reasonable accommodation to individuals with disabilities. If, because of a medical condition or disability, you need a reasonable accommodation for any part of the application process, or to perform the essential functions of a position, please send an e-mail to us.reasonableaccommodations@novartis.com or call +1(877)395-2339 and let us know the nature of your request and your contact information. Please include the job requisition number in your message.Salary Range$132,300.00 - $245,700.00Skills DesiredBusiness Development, Collaboration, Customer Experience (CX), Deal Origination, Evaluation Methodology, Finance, Life Science, Media Campaigns, Merchandising, Proactive Behavior, Sales, Sales Techniques, Search Techniques, Transaction Execution, Waterfall Model, Wireless CommunicationsSummaryLocation: Cambridge (USA)Type: Full time

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 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

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. 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

In this role, you will be responsible for the design and aerothermal analysis of the compressor for a micro-gas turbine. You will work closely with the systems / performance engineer, combustor and turbine designers, structural engineers, and the head of manufacturing to ensure that the overall design of the compressor meets the system needs. An ideal candidate will be able to own both aerothermal (main gas path) and thermomechanical (structural) aspects of the compressor design and will be familiar with the overall design cycle as well as with detailed aerodynamic design for compressors. • Annulus and blade design (aerothermal) of the compressor, • CFD analysis of design-point performance, • Generation of compressor map, • Aerothermal load transfer for structural analysis, • Solid foundation in fluid mechanics, • Proactively engages others to "self-start" and to progress work, • Solid foundation in turbomachinery aerodynamics, • Experience in full design cycle for a compressor / turbomachinery sub-system, • Familiar with the influence of clearances, bleed flows and diffuser design on compressor efficiency, • Familiar with cycle design and determination of operating line, surge margin, etc., • Competent FEA practitioner, able to own aerothermal and thermomechanical design & analysis, • Experience in fast-paced R&D or startup environments, with rapid prototype iteration times, • Experience in full design cycle for a Turbine / turbomachinery sub-system, • Competitive compensation and stock options, • Private Medical & Dental, • Cycle to work scheme The mission of Cambridge Aerospace is to eliminate the risk of existential war in Europe. That risk comes from the air - low-cost drones and cruise missiles now manufactured in colossal volumes by our adversaries, ballistic missiles capable of ending the world as we know it, and a new generation of hypersonic weapons that evade every existing defence. We’re building low-cost interceptors that defeat these threats, and the surveillance systems to detect and target them - at a cost taxpayers can afford, at the pace the threat demands, and with sovereign supply chains that assure delivery. We have immense backing from the world’s best venture funds. We’re one of the fastest growing defence technology companies in history. And we’re hiring the best engineers to prosecute the mission. If you are in the top 1%, willing to work harder than you ever have, build technology better than anyone else, and contribute to the defining challenge of our time, now is the time to join us. We’re committed to building a diverse and inclusive workplace. If you’re excited about this role but don’t meet every requirement, #J-18808-Ljbffr

We're hiring an Application Software Engineer to own the design and development of our ground control station software, from early concept through integration and test. You'll define the application architecture and work with embedded, systems, and test engineers to build reliable, responsive operator tools. You'll take features from prototype to field-deployed software, and your work will directly shape how operators monitor, command, and trust our systems. • Work with the wider engineering team to identify and define the architecture of our application software, • Build fast, well-tested backend services in Rust OR operator interfaces in Flutter, with accreditation in mind, • Design clear, robust interfaces between the ground station, vehicles, sensors, and third-party systems, • Work with operators and stakeholders to turn operational needs into intuitive, low-workload user interfaces, • Scope and expand our CI, automated testing, and release infrastructure, • Contribute to technical decisions, code reviews and engineering best practice across our applications, • Experience developing ground control systems, with evidence of technical ownership from requirements through integration and test, • Strong commercial experience with Rust, including asynchronous and concurrent application design OR, • Experience building production user interfaces in Flutter/Dart, • A good understanding of UI/UX principles, especially for operator-facing, safety- or mission-critical displays, • Experience integrating with external hardware and systems over network and serial interfaces, including fault handling and recovery, • Practical experience with field trials, system integration, log and data analysis, and fault diagnosis, • A strong grasp of software engineering practice: automated testing, version control, CI/CD, and code review, • Experience with Tokio and the async Rust ecosystem, • Experience with gRPC and Protocol Buffers for service and client-server interfaces, • Experience with other ground station or command-and-control systems, for example UAS ground control, mission planning, telemetry and video handling, map-based situational awareness, or radar and sensor integration, • Familiarity with vehicle communication protocols, • Experience building simulation, hardware-in-the-loop, or integration test environments for automated validation, • Experience with Linux-based deployment, secure software delivery, or packaging for fielded systems, • Experience in a fast-paced R&D or startup environment with rapid prototype iteration, • Competitive compensation and stock options, • Private Medical & Dental, • Cycle to work scheme The mission of Cambridge Aerospace is to eliminate the risk of existential war in Europe. That risk comes from the air - low-cost drones and cruise missiles now manufactured in colossal volumes by our adversaries, ballistic missiles capable of ending the world as we know it, and a new generation of hypersonic weapons that evade every existing defence. We're building low-cost interceptors that defeat these threats, and the surveillance systems to detect and target them - at a cost taxpayers can afford, at the pace the threat demands, and with sovereign supply chains that assure delivery. We have immense backing from the world's best venture funds. We're one of the fastest growing defence technology companies in history. And we're hiring the best engineers to prosecute the mission. If you are in the top 1%, willing to work harder than you ever have, build technology better than anyone else, and contribute to the defining challenge of our time, now is the time to join us. We're committed to building a diverse and inclusive workplace. If you're excited about this role but don't meet every requirement, we still encourage you to apply. #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 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. • 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. 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

We are looking for a Modelling and Simulation Engineer to own the integrated vehicle model for a new interceptor. You will define its architecture, build component models, and integrate models from across the engineering team. You will make the simulation trusted, repeatable, and useful from concept design through flight test. • Own the integrated 6-DoF vehicle model and its technical roadmap, • Define the model architecture, interfaces, fidelity and configuration strategy, • Build models for actuators, sensors, launchers and other vehicle components, • Integrate GNC algorithms and component models supplied by engineering teams, • Correlate models against component, ground and flight-test data, • Provide simulation environments supporting MIL, Monte Carlo analysis, HIL and distributed simulation, including integration with system-of-systems analysis tools, • Modelling and simulation experience in aerospace, including evidence of technical ownership of a workstream, • Experience developing physics-based vehicle models, supported by strong knowledge of rigid-body dynamics, flight mechanics and numerical methods. Experience with missiles, interceptors, rockets or high-speed vehicles is a plus, • Ability to coordinate across aerodynamics, GNC, propulsion, radar, software and test, • Sound software‑engineering practices, including configuration management and automated testing. Familiarity with parallel or distributed workflows is a plus, • Experience with model validation and qualification is highly desirable. System identification knowledge is a plus, • Strong MATLAB/Simulink skills. Programming knowledge in Python, C++ or similar for developing simulation and analysis tools is a plus, • Experience in fast-paced R&D or startup environments, with rapid prototype iteration times, • Competitive compensation and stock options, • Private Medical & Dental, • Cycle to work scheme The mission of Cambridge Aerospace is to eliminate the risk of existential war in Europe. That risk comes from the air - low-cost drones and cruise missiles now manufactured in colossal volumes by our adversaries, ballistic missiles capable of ending the world as we know it, and a new generation of hypersonic weapons that evade every existing defence. We’re building low-cost interceptors that defeat these threats, and the surveillance systems to detect and target them - at a cost taxpayers can afford, at the pace the threat demands, and with sovereign supply chains that assure delivery. We have immense backing from the world’s best venture funds. We’re one of the fastest growing defence technology companies in history. And we’re hiring the best engineers to prosecute the mission. If you are in the top 1%, willing to work harder than you ever have, build technology better than anyone else, and contribute to the defining challenge of our time, now is the time to join us. We’re committed to building a diverse and inclusive workplace. If you’re excited about this role but don’t meet every requirement, we still encourage you to apply. #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

Luminance is the pioneer of Contract Intelligence for enterprise. Backed by internationally renowned venture capital firms and named in both the Forbes AI 50 list of “Most Promising Private AI Companies in the World” and the Inc. 5000 list of “Fastest Growing Companies in America”, Luminance is transforming how businesses understand and act on the intelligence contained in their contracts. We are expanding rapidly across global markets and supporting enterprise customers in more than 60 countries, including AMD, National Grid, LG and DHL. We are looking for an FP&A Lead to build the financial planning and insight capability needed to support that growth. This is a true build-it role. As Luminance’s first dedicated FP&A hire, you will build the capability from the ground up—determining what good looks like and creating the models, reporting, systems and operating rhythms that connect our strategy, operating plans and financial performance. Reporting to the VP Finance and working alongside the Financial Controller, you will own Luminance’s forward-looking financial view: budgeting, forecasting, long-range planning, performance analysis and management reporting. We are looking for a hands-on finance operator who sees AI as a core part of the modern finance toolkit—using it to strengthen forecasts, challenge assumptions, automate repetitive work and uncover better insight. This will initially be a senior individual contributor role, offering significant ownership and exposure across the business, with the opportunity to build the wider FP&A team as Luminance scales. Own financial planning — build and maintain the models underpinning Luminance’s annual budget, rolling forecasts, headcount and cost plans, and longer-term strategic plan. Lead planning cycles end to end — work with department leaders to create ambitious, credible plans and keep the process rigorous and on track. Turn performance into action — analyse actual results against plan, identify the underlying drivers and translate them into clear recommendations. Create a trusted view of the business — define and track the KPIs that matter, building reporting and dashboards that give leadership timely, reliable insight. Partner across Luminance — help leaders translate company priorities into financial plans, understand performance and make informed resource decisions. Support important decisions — provide executive- and board-level analysis on financial performance, investment priorities and the company’s key value drivers. Model future scenarios — evaluate the financial implications of different growth, hiring, investment, market expansion and fundraising scenarios. Build an FP&A capability that scales — continually improve the processes, systems and AI-enabled workflows that make FP&A faster, more accurate and more valuable. Significant experience in FP&A, corporate finance or financial planning and analysis. A track record of building or significantly improving FP&A models, processes and reporting within a scaling business. Experience in a high-growth B2B SaaS, enterprise software or technology company, with a strong understanding of SaaS metrics and recurring-revenue economics. Excellent financial modelling, forecasting and scenario-analysis skills. Strong financial and commercial judgement, with the ability to challenge assumptions and turn complex analysis into clear recommendations. Credibility with senior stakeholders and the ability to communicate financial concepts clearly to non-finance audiences. A hands-on, high-ownership approach—you are as comfortable building the model as presenting its conclusions. Practical experience using modern systems, data, AI and automation to improve the quality and speed of finance work. • Experience supporting board, investor or fundraising processes., • A professional accounting qualification or relevant background in investment banking, consulting or investing., • Familiarity with business-intelligence tools and financial-planning platforms. #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. #J-18808-Ljbffr

Nuclera is a venture-backed, founder-driven biotech company headquartered in Cambridge, UK with significant operations in Boston, USA. Our purpose is to improve human health by making proteins accessible to life science researchers around the world. In 2024, we launched eProtein Discovery™ - a breakthrough protein expression, characterization, and purification platform, consisting of cloud-based software and a lab-based instrument (with proprietary cartridges and consumables). Since then, we have established a growing installed base in North America, Europe and Asia, receiving numerous awards and customer accolades. We are looking for an experienced full-stack software engineer to work on our customer-facing cloud and instrument software as well as the internal tools that support our in-house protein screening services. You’ll be involved in the full software development lifecycle, including working closely with biologists and other stakeholders to define use-cases and requirements, translating those requirements into technical designs, implementing those designs in code and writing tests to verify its correctness. No prior knowledge of biology is necessary, but curiosity and an eagerness to learn is critical. About the role This role is for you if you: • Have at least 5 years industry experience as a software engineer., • Are confident working across backend, frontend, dev-ops and QA., • Have a product mindset, with a track record of gathering requirements, owning features end to end, and working directly with key stakeholders to deliver the right outcome., • Are ready to take ownership of code and keep it up-to-date with the latest best-practices. Requirements • Java and Python, • REST and Websocket APIs, • A backend web framework (e.g. Micronaut, Spring), • Google Cloud or an equivalent cloud service provider (e.g. AWS, Azure), • React or an equivalent frontend technology (e.g. Angular), • Containerisation (e.g. Docker, Podman), • Automated testing (e.g. JUnit, Mockito, pytest), • Automated build and deployment pipelines, • Version control in Git, • Claude Code or an equivalent AI coding assistant, • Experience with bioinformatics data processing pipelines What we offer: In addition to competitive salaries, we offer a range of benefits including: • Company bonus scheme of 5%, • Life insurance, • Private medical insurance and cash plan, • 25 days' annual leave + Bank Holidays, • Enhanced employer's pension contributions, • Enhanced maternity and paternity Leave, • Investment in professional development and learning, • Fresh fruit, tea, coffee, and snacks in the office, • Organised summer events for staff #J-18808-Ljbffr

As Senior Buyer - Indirect at Cambridge Aerospace, you will report to the Category Manager - Indirect and manage day-to-day sourcing and supplier activity for a broad portfolio of non-production goods and services. Working with stakeholders across the business, you will turn approved requirements into well-controlled, commercially sound and timely supplier arrangements. You will also support the operational purchasing needs of Product Development, helping ensure orders are placed accurately and on time. The role supports multiple company sites, and some business travel will be required. • Manage end-to-end sourcing activity for assigned indirect categories, from requirement clarification and market research through RFQ or tender, evaluation, negotiation, recommendation and purchase or contract placement., • Support the operational needs of Product Development by processing and progressing approved orders, coordinating with requestors and suppliers, and helping resolve delivery, pricing or order-related issues., • Support categories including IT, software, SaaS, cloud, cybersecurity, professional services, contractors, recruitment, facilities, marketing, training, travel, legal, finance, utilities, capital equipment, leases, logistics and other business services., • Work with stakeholders to define scope, specifications, service levels, delivery expectations, budgets and evaluation criteria before approaching suppliers., • Obtain competitive quotations, conduct commercial comparisons and negotiate price, payment terms, service levels, lead times, renewal terms and other relevant conditions., • Coordinate supplier onboarding, due diligence and internal approvals, ensuring no commitment is made before the required procurement and financial controls are complete., • Monitor supplier delivery and service performance, resolve commercial issues and support structured reviews and improvement actions., • Identify opportunities to consolidate suppliers, standardise requirements, reduce cost, improve terms and increase use of preferred suppliers., • Support the Category Manager - Indirect with spend analysis, category plans, sourcing pipelines, market intelligence, supplier risk and stakeholder updates., • Strong experience as a Buyer or Senior Buyer managing indirect goods and services in a commercial environment., • Demonstrable experience running competitive RFQs or tenders, evaluating supplier proposals and negotiating commercial terms., • Good understanding of purchase orders, Statements of Work, subscriptions, licences, services agreements and recurring supplier commitments., • Strong numerical and analytical skills, with the ability to build clear commercial comparisons and recommendations., • Organised and accurate, with effective prioritisation across multiple stakeholders and sourcing activities., • Confident communicator who can challenge requirements constructively and maintain positive stakeholder relationships., • Practical, proactive and comfortable working in a fast-growing environment where processes continue to evolve., • Experience in defence, aerospace, manufacturing, engineering, technology or another regulated environment., • Experience sourcing IT, SaaS, professional services, facilities, recruitment, travel or other corporate services., • Working knowledge of supplier due diligence, data protection, information security and contract-risk considerations., • CIPS qualification, progress toward CIPS, or equivalent procurement training., • Experience using ERP, e-procurement, contract-management or spend-analysis tools., • Competitive compensation and stock options, • Private Medical & Dental, • Cycle to work scheme, • Lunchtime catering on site The mission of Cambridge Aerospace is to eliminate the risk of existential war in Europe. That risk comes from the air - low-cost drones and cruise missiles now manufactured in colossal volumes by our adversaries, ballistic missiles capable of ending the world as we know it, and a new generation of hypersonic weapons that evade every existing defence. We are building low-cost interceptors that defeat these threats, and the surveillance systems to detect and target them - at a cost taxpayers can afford, at the pace the threat demands, and with sovereign supply chains that assure delivery. We have immense backing from the world's best venture funds. We are one of the fastest growing defence technology companies in history. And we are hiring the best people to prosecute the mission. If you are in the top 1%, willing to work harder than you ever have, build capability better than anyone else, and contribute to the defining challenge of our time, now is the time to join us. We are committed to building a diverse and inclusive workplace. If you are excited about this role but do not meet every requirement, we still encourage you to apply. #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

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

As an embedded software engineer, you will be responsible for delivering embedded software on a range of platforms for applications from flight control to radar processing. • Identify and define the architecture of embedded software in conjunction with the wider engineering team, • Scope and expand our CI and automated testing and release infrastructure, • Build high-performance, deterministic and well-tested embedded systems with a view to accreditation, • Definition of interfaces between software elements (APIs) and over communications interfaces (protocols), • Excellent embedded C/C++/Rust skills, • Experience developing for safety-critical systems, i.e., avionics, medical or automotive, • Excellent analytical, communication, and teamwork capabilities, • Experience in fast-paced R&D or startup environments, with rapid prototype iteration times, • Experience with bare-metal, RTOS (i.e., NuttX, ThreadX, RTIC, Zephyr, FreeRTOS, VxWorks, INTEGRITY), and embedded Linux, • Experience with STM32, Xilinx Zynq Ultrascale+, etc., • Experience with flight controllers, • Competitive compensation and stock options, • Private Medical & Dental, • Cycle to work scheme The mission of Cambridge Aerospace is to eliminate the risk of existential war in Europe. That risk comes from the air – low-cost drones and cruise missiles now manufactured in colossal volumes by our adversaries, ballistic missiles capable of ending the world as we know it, and a new generation of hypersonic weapons that evade every existing defence. We’re building low-cost interceptors that defeat these threats, and the surveillance systems to detect and target them – at a cost taxpayers can afford, at the pace the threat demands, and with sovereign supply chains that assure delivery. We have immense backing from the world’s best venture funds. We’re one of the fastest growing defence technology companies in history. And we’re hiring the best engineers to prosecute the mission. If you are in the top 1%, willing to work harder than you ever have, build technology better than anyone else, and contribute to the defining challenge of our time, now is the time to join us. We’re committed to building a diverse and inclusive workplace. If you’re excited about this role but don’t meet every requirement, we still encourage you to apply. #J-18808-Ljbffr

HyperLight is at the forefront of the commercialization of thin-film lithium niobate (TFLN) integrated photonics — a material and process technology that is enabling high-performance, scalable optical components across AI/datacom infrastructure, hyperscale computing, quantum computing, sensing, and beyond. Founded in 2018 and backed by leading venture capital, we’ve built a team and a platform focused on real-world mass deployment of TFLN photonics technology. At the core of our work is the TFLN Chiplet™ platform: a modular architecture designed for scalability, manufacturability, and integration into complex systems. We partner with our customers and suppliers from concept and prototyping through mass production. We believe in integrity, innovation, collaboration, and pragmatic solutions, and we take pride in tackling hard problems with curiosity, humility, and a deep sense of care for one another. TFLN photonic circuits are built on the same design principles as silicon and other platforms, but the material's anisotropic and EO behavior opens up a largely unexplored design space. Our growing team is looking for a Staff or Senior Staff Photonics Design Engineer to lead the design of device components on our platform, with a particular focus on optical interfaces. We welcome strong designers from SOI, III-V, and other platforms. Technical depth matters more to us than prior experience with any one material. The work spans passive and active devices, simulation and measurement, and close collaboration with process, test, and product teams. People who do well here enjoy that range: they pick up unfamiliar topics quickly, bring ideas over from adjacent fields, and like seeing a design through every stage rather than handing it off. • Lead the design of high-performance, robust device components, including fiber and free-space optical interfaces such as grating couplers and edge couplers, along with splitters, optical taps, crossings, and thermo-optic phase shifters, • Own component design flows end to end: defining requirements with product teams and customers, selecting architectures, simulation, layout, tapeout, measurement, and handoff to production, • Apply optimization-driven and statistical design methods so that components are robust to fabrication variation and hold up at production volumes, • Simulate component designs using tools like Tidy3D, Lumerical, COMSOL, and HFSS, as well as homegrown tools in Python that you will help write, • Develop and perform rigorous measurement techniques to characterize component performance, and analyze measurement and simulation data for performance and robustness, • Collaborate with process engineering to improve our fabrication processes for optimal device performance, • Serve as a technical expert within HyperLight, mentoring other engineers and helping guide our engineering decisions and technical roadmap, • Either a Ph.D. in Electrical Engineering, Physics, Optics, or a related field with 3+ years of relevant industry experience, or 9+ years of relevant industry experience with a B.S. or M.S., • Prior ownership of photonic component design flows in foundry processes, with multiple designs taken through tapeout and characterization, • Strong background in advanced design techniques such as optimization-driven design and statistical modeling, including design of experiments, • Strong expertise in simulation of PIC components using tools like Tidy3D, Lumerical, COMSOL, Synopsys OptoDesigner, etc, • Hands‑on experience characterizing photonic integrated circuits using instruments like swept lasers, fiber alignment stages, VNAs, BERTs, oscilloscopes, etc, • Experience writing software using scripting languages like Python or Julia, • Demonstrated record of innovation and achievement, e.g. product designs, publications, or patents, • Excellent organizational and communication skills, and comfort working with cross-disciplinary teams Exceptional candidates finishing a Ph.D. with directly relevant design and tapeout experience will also be considered. • Design experience with grating couplers, grating emitters, or other fiber and free-space optical interfaces, including apodized, chirped, or dual‑polarization designs, • Familiarity with fabrication processes such as lithography, etch, and deposition, and how they shape device performance, • Experience with photonic layout tools such as Cadence Virtuoso or gdsfactory, • Comfortable with git‑based version control, • Experience with active photonic devices such as thermo‑optic phase shifters, • Deep design expertise on high‑index‑contrast platforms such as silicon‑on‑insulator (SOI), or on TFLN, • Competitive market‑based compensation (Salary range $160,000–$250,000 per year), • New hire equity/option grant, • Comprehensive health coverage, including medical, vision, and dental plans for individuals and families, • 401(k) retirement plans with employee matching, • Paid parental leave, • Life and disability insurance, • Commuter benefits and subsidies, • Professional growth and mentorship opportunities This position is onsite at our office in Cambridge, MA. Authorization to work in the United States is required. HyperLight is an equal opportunity employer. All applicants will be considered for employment without attention to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status. HyperLight is committed to providing reasonable accommodations to individuals with disabilities during our hiring process. If you need assistance or an accommodation, please contact us at . #J-18808-Ljbffr

As an engineer in the Internal Tooling team, you'll build the software that runs on our physical devices. That includes test stations, manufacturing rigs, lab equipment and the computers that control them. Your applications control and read from custom hardware and instruments, then reliably send that data to backend services where the rest of the company can use it. Our users are our own colleagues, across engineering, manufacturing, and all other departments. Feedback loops are extremely fast; our work exists to enable the company to move faster and increase our resilience. You'll work across hardware interfaces, device applications and backend integration. You should be comfortable picking up new protocols, devices and technologies as projects require. • Lead the design of applications and services that run on physical devices and talk to custom hardware and lab instruments, • Build reliable data pipelines from devices to backend APIs that cope with network outages, hardware faults and restarts without losing data, • Design device software for easy deployment, configuration, updates and remote diagnosis, • Work with hardware, test and manufacturing engineers to turn physical processes into dependable software, • Work with backend and platform teams to define APIs and data contracts between devices and services, • Mentor teammates and share hardware-integration best practices, • Look for opportunities to automate and instrument more of our physical processes, • Strong proficiency in Python, C++ or similar (e.g. Rust, Go, C#), • Experience writing software that talks to hardware over interfaces such as serial, USB, CAN, and Ethernet/TCP, • Experience building clients that send data to backend APIs (e.g. REST), including retries, buffering and offline operation, • Solid Linux experience: services (systemd), networking, permissions and device access, • Understanding of concurrency, async I/O and robust error handling in long-running applications, • Experience owning the full development lifecycle: requirements, design, implementation, testing, documentation and handover, • Comfortable with standard engineering practices: Git, code review, agile planning, • Embedded firmware or microcontroller development (e.g. STM32), • Device fleet management: provisioning, over-the-air or remote updates, configuration management, device monitoring, • Containerised deployments on edge devices with Docker, • Testing hardware-dependent code: hardware-in-the-loop (HITL) testing, simulators and mocks, • Improving CI/CD pipelines, including cross-compilation and building device images, • Familiarity with backend storage and cloud services (e.g. Postgres, AWS S3, DynamoDB, Lambda), • Simple operator UIs (e.g. tkinter, web-based local UIs, TypeScript), • Using AI tools to speed up your work while keeping quality high and your own understanding solid, • Manufacturing, aerospace, or other hardware-focused industries, • Start-ups or other fast-paced engineering environments, • Competitive compensation and stock options, • Private Medical & Dental, • Cycle to work scheme, • Lunchtime catering on site The mission of Cambridge Aerospace is to eliminate the risk of existential war in Europe. That risk comes from the air – low-cost drones and cruise missiles now manufactured in colossal volumes by our adversaries, ballistic missiles capable of ending the world as we know it, and a new generation of hypersonic weapons that evade every existing defence. We’re building low-cost interceptors that defeat these threats, and the surveillance systems to detect and target them – at a cost taxpayers can afford, at the pace the threat demands, and with sovereign supply chains that assure delivery. We have immense backing from the world’s best venture funds. We’re one of the fastest-growing defence technology companies in history. And we’re hiring the best engineers to prosecute the mission. If you are in the top 1%, willing to work harder than you ever have, build technology better than anyone else, and contribute to the defining challenge of our time, now is the time to join us. We’re committed to building a diverse and inclusive workplace. #J-18808-Ljbffr

As a Test Equipment Development Engineer you will design, develop, commission and maintain the automated and semi-automated test equipment used to test our products. Your role will a mix of hands-on engineering with real-world production and development testing. You will own the development of bespoke test solutions spanning electronics, electro-mechanical systems, missile subsystems, propulsion hardware and production acceptance testing. Working closely with Product, Electronics, Systems, Manufacturing and Verification & Validation teams, you will develop automated-test-equipment (ATE), hardware-in-the-loop systems, development rigs and production test stations from initial concept through to deployment and ongoing support. Responsibilities • Design and develop bespoke automated and semi-automated test equipment from requirements capture through architecture, build, integration, commissioning and operational deployment., • Develop electrical, electronic and mechanical test interfaces including fixtures, harnesses, break-out equipment, instrumentation, sensors, signal conditioning, switching hardware and data acquisition systems., • Develop automated test sequences, control software and operator interfaces., • Implement automated data acquisition, monitoring, pass/fail determination, traceable data capture, analysis and reporting across development and production test environments., • Develop production-ready end-of-line test equipment, improving throughput, reliability, maintainability and operator usability while supporting the transition of R&D rigs into controlled production systems., • Provide technical ownership for deployed test equipment including troubleshooting, configuration control, continuous improvement, engineering standards, documentation and external supplier engagement. Requirements • Experience developing automated or semi-automated test equipment, automated-test-equipment (ATE), or complex test systems within aerospace, defence, automotive, space, medical or another high-reliability engineering environment., • Proven experience taking test equipment from requirements capture and concept through to operational deployment., • Practical knowledge of instrumentation, measurement techniques, signal integrity, sensors, DAQ systems, hardware interfaces and fault-finding., • Experience with Dewesoft, LabVIEW, TestStand, Python, C#, MATLAB or PLC programming., • Experience with automated sequencing, data logging, hardware control and report generation., • Ability to derive practical test solutions from system and verification requirements. Preferred Qualifications/Experience • Degree, HNC/HND, apprenticeship or equivalent professional experience in Electrical, Electronic, Software, Aerospace, Mechanical Engineering or a related discipline., • Experience developing test equipment for missile, UAV, propulsion, avionics, electro-mechanical or other complex systems., • Experience with hardware-in-the-loop testing (HIL), production line automation, databases, serial buses, network interfaces and test equipment lifecycle support., • Experience designing test equipment for repeatable manufacturing use, including maintainability, operator usability, configuration control and equipment safety. What We Offer • Competitive compensation and stock options, • Private Medical & Dental, • Cycle to work scheme About Us The mission of Cambridge Aerospace is to eliminate the risk of existential war in Europe. That risk comes from the air: low-cost drones, cruise missiles, ballistic missiles and hypersonic weapons that existing defence systems are too expensive, too scarce or too slow to defeat at scale. We are building a new generation of low-cost interceptors and supporting systems designed to defend against those threats at a price point that allows them to be deployed in meaningful numbers. Our aim is to deliver capability quickly, build sovereign European supply chains and fundamentally change the economics of air defence. Backed by leading venture funds, Cambridge Aerospace is growing rapidly and bringing together some of the best engineers and operators to solve one of the most important technical challenges of our generation. If you are in the top 1%, willing to work harder than you ever have, build technology better than anyone else, and contribute to the defining challenge of our time, now is the time to join us. Our Commitment to Inclusion We’re committed to building a diverse and inclusive workplace. If you’re excited about this role but don’t meet every requirement, we still encourage you to apply. We’re interested in your practical ability, potential and willingness to learn, not just a traditional academic or career path. #J-18808-Ljbffr

As an engineer in the Platform team you will be building the foundational features to enable the development and hosting of applications to a design that promotes security, reliability, operational efficiency, and cost. Your users are your own colleagues, across engineering, manufacturing, and all other departments. Feedback loops are extremely fast; our goal is to enable the company to move faster and increase our resilience. You will need to be comfortable working across a wide array of technologies, and picking up those which you are not yet familiar with. These may be cloud providers, configuration management languages, or SaaS services designed to monitor and enhance our platform. • Design and own the architecture of our cloud platform, including account/subscription structure, networking, identity, compute, data and observability, balancing security, reliability, cost and operational efficiency, • Build self-service capabilities (reusable IaC modules, templates, pipelines and tooling) that teams use independently, • Build security into the platform through secure network design, least-privilege access, and using security monitoring tools, • Work with engineering teams to understand their needs and turn them into the platform architecture, • Identify opportunities to enable other teams by introducing new tools or improving existing systems, • Mentor engineers and raise the platform and infrastructure skills of the wider team, • Experience designing cloud platform architecture from scratch in AWS and/or Azure, including multi-account/subscription structure, networking and identity, • Strong Infrastructure as Code skills (e.g. Terraform), including authoring reusable, versioned modules used by other teams, • Proficiency in a programming language such as Python or TypeScript, used to build platform tooling, automation or services, • Experience designing CI/CD systems and deployment patterns for diverse requirements, • Experience designing hosting patterns for containerized and serverless workloads, and managed data services (e.g. PostgreSQL), • Experience in end-to-end of system development: Requirements gathering, design, implementation, testing, documentation, & handover, • Experience working in industry following industry expected standards such as: Git, project planning, agile workflows, • Experience with both Azure & AWS, • Designing observability platforms and FinOps, • Automated testing of infrastructure code, • Designing or building data platform infrastructure, • Configuration management (e.g. Ansible), • Experience in the manufacturing and/or aerospace industry, • Using AI tools to speed up your work while keeping quality high and your own understanding solid, • Manufacturing, aerospace, or other hardware-focused industries, • Start-ups or other fast-paced engineering environments, • Competitive compensation and stock options, • Private Medical & Dental, • Cycle to work scheme The mission of Cambridge Aerospace is to eliminate the risk of existential war in Europe. That risk comes from the air – low-cost drones and cruise missiles now manufactured in colossal volumes by our adversaries, ballistic missiles capable of ending the world as we know it, and a new generation of hypersonic weapons that evade every existing defence. We’re building low-cost interceptors that defeat these threats, and the surveillance systems to detect and target them – at a cost taxpayers can afford, at the pace the threat demands, and with sovereign supply chains that assure delivery. We have immense backing from the world’s best venture funds. We’re one of the fastest growing defence technology companies in history. And we’re hiring the best engineers to prosecute the mission. If you are in the top 1%, willing to work harder than you ever have, build technology better than anyone else, and contribute to the defining challenge of our time, now is the time to join us. We’re committed to building a diverse and inclusive workplace. If you’re excited about this role but don’t meet every requirement, we still encourage you to apply. #J-18808-Ljbffr

3 weeks ago Be among the first 25 applicants We're looking for a skilled Frontend Developer with expertise in TypeScript and React to build and enhance the cutting-edge design and analysis tools behind our revolutionary eProtein Discovery platform. Join us in shaping the future of protein prototyping technology! Nuclera is a venture-backed, founder-driven biotech company headquartered in Cambridge, UK with significant operations in Boston, USA. The company's mission is to improve human health by making proteins accessible. We are beginning this mission through the launch of our eProtein Discovery platform. eProtein Discovery is a breakthrough protein expression, characterization, and purification technology, consisting of a lab-based benchtop instrument, cloud-based software and proprietary cartridges and consumables. The goal is to enable rapid protein access to life science researchers around the world. We are looking for an experienced frontend or full-stack software engineer to join our team of engineers working on both the cloud-based and on-instrument software used by our customers to design and execute experiments on the eProtein Discovery platform. You'll be involved in the full software development lifecycle, including gathering requirements from biologists and other stakeholders, translating those requirements into UI designs and working with backend engineers to implement those designs in code. No prior knowledge of biology is necessary, but curiosity and an eagerness to learn is critical. The culture in the software team supports in-office and hybrid working. About the role: • Have at least 3 years industry experience as a software engineer., • Be ready to take ownership of code and keep it up-to-date with the latest best-practices, • Have a passion for translating complex requirements into simple, easy-to-understand user interfaces, • Have a strong interest in backend development, • Be curious about protein synthesis Requirements Essential You should have experience with the following languages, tools and methodologies: • React with state management libraries such as Redux, • Typescript or Flow typing and modern ES6 javascript, • HTML and CSS, • Webpack, • Version control with Git, • Unit testing with Jest, React testing library or similar framework, • Java and Python, • Knowledge of CI/CD pipelines, • Agile software development and code reviews Desirable It would be beneficial for you to have some experience with: • Data visualisation libraries e.g. D3, websockets and storybook, • Design tools such as Figma and Balsamiq, • Cloud technologies such as Kubernetes and Google Cloud Platform Benefits What we offer: In addition to competitive salaries, we offer a range of benefits including: • Company bonus scheme of 5%, • Life insurance, • Private medical insurance and cash plan, • 25 days' annual leave + Bank Holidays, • Enhanced employer's pension contributions, • Enhanced maternity and paternity Leave, • Investment in professional development and learning, • Access to amenities on Vision Park and in Histon, • Fresh fruit, tea, coffee, and snacks in the office, • Organised team events, • Mid-Senior level, • Full-time, • Engineering and Information Technology, • IT Services and IT Consulting Referrals increase your chances of interviewing at Nuclera by 2x Get notified about new Software Engineer jobs in Cambridge, England, United Kingdom. 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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. 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 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

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 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. What you will do • 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. 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

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. 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., • 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