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Location: London (hybrid) Level: Director Reporting to: VP of Engineering The company This is a well-funded, fast-growing B2B SaaS company that's become one of the most talked-about in its space. They've hit unicorn status and are now in the middle of a significant scaling phase — the engineering org is ~50 people today and is expected to grow to 150+ over the next 12 months. The product is used by some of the world's fastest-growing companies. The team is distributed across the UK and Europe, with a strong technical bar and a no-nonsense engineering culture. People here came from Google, Meta, Vercel, and high-growth scaleups. The VP of Engineering spent 9+ years at Google managing infrastructure teams at significant scale before joining at an early stage. This is not a corporate environment. Founders are hands-on, technical, and have high standards. The people who thrive here have opinions, make decisions, and lead from the front. The role You'll be joining as one of two Engineering Directors, sitting between the VP of Engineering and a layer of engineering managers who each lead autonomous product squads. You'll own a significant portion of the engineering organisation and be accountable for how it performs, scales, and grows. This is a player-coach role in the truest sense of leadership — not hands-on coding, but close enough to the technical work that you can hold your own in a systems design conversation, challenge architectural decisions, and earn the respect of strong engineers. What you'll be doing: • Managing a set of engineering managers and senior ICs, acting as their line manager, coach, and technical sounding board, • Owning the health and output of multiple product squads — delivery, quality, morale, and hiring, • Partnering with the VP of Engineering on org design, technical strategy, and how engineering scales from here, • Driving hiring across your org — you'll be building something meaningful over the next 12 months, • Feeding into cross-functional decisions alongside product, design, and go-to-market leadership, • Being the person who makes the call when things are ambiguous, moves fast when speed matters, and is comfortable having hard conversations What we're looking for Must-haves: • Experience managing engineering managers (not just individual contributors) — you've operated a layer of abstraction above the squads, • A background that combines time at a well-engineered large company and time at a high-growth B2B SaaS scaleup — you understand both how great engineering is built and what it takes to ship fast with limited resources, • The technical depth to do a credible systems design conversation — you don't need to write production code but you need to understand the tradeoffs engineers are making, • Experience in the 50–200 engineer range — you've either been through a scaling journey like this or are close enough to it that you understand what breaks and why, • Comfortable with ambiguity, fast-changing priorities, and a culture where the people above you are also deeply capable Strong signals: • B2B SaaS domain experience — RevOps, developer tooling, or anything close to the product surface, • You've hired well at pace — you know what great looks like and you can move quickly to close, • You've let people go when it was the right thing to do, • You've worked in a distributed team and made it work, • You're the kind of person who's done their research before walking into a room Not a fit if: • You're looking for a quiet, process-heavy environment, • Your default mode is "let me set up a committee", • You describe your leadership style primarily as "servant leader" and leave it there, • You haven't been close to the technical work in several years The interview process • Track record conversation (45 min, remote), • Incident response / technical leadership (45 min, remote) Compensation Competitive base salary, meaningful equity, and benefits. Range discussed on first conversation.

Full-time | Remote with trips to Silicon Valley office | Reports to Founders Introduction: • Join us at Fastino as we build the next generation of LLMs. Our team, boasting alumni from Google Research, Apple, Stanford, and Cambridge is on a mission to develop specialized, efficient AI., • Fastino's GLiNER family of open source models has been downloaded more than 5 million times and is used by companies such as NVIDIA, Meta, and Airbnb, • Fastino has raised $25M (as featured in TechCrunch) through our seed round and is backed by leading investors including Microsoft, Khosla Ventures, Insight Partners, Github CEO Thomas Dohmke, Docker CEO Scott Johnston, and others. What You’ll Work On: • Experiment with novel language model architectures, helping drive and execute Fastino's research roadmap, • Optimize Fastino’s multimodal models to improve response quality, instruction adherence, and overall performance metrics, • Architect data processing pipelines, implementing filtering, balancing, and captioning systems to ensure training data quality across diverse content categories, • Implement reinforcement learning techniques including Direct Preference Optimization and Generalized Reward Preference Optimization to align model outputs with human preferences and quality standards, • Build robust and real-world motivated evaluations, • Partner with Fastino engineering team to ship model updates directly to customers, • Establish best practices for code health and documentation on the team, to facilitate collaboration and reliable development What We’re Looking For: • Required - Great velocity for building and shipping agents / AI products., • Optional - Advanced degree (Master's or PhD) in Computer Science, Artificial Intelligence, Machine Learning, or related technical discipline with concentrated study in deep learning and computer vision methodologies, • Optional - Demonstrated ability to do independent research in Academic or Industry settings, • Optional - Substantial industry experience in large-scale deep learning model training, with demonstrated expertise in at least one of Large Language Models, Vision-Language Models, Diffusion Models, or comparable generative AI architectures, • Optional - Comprehensive technical proficiency and practical experience with leading deep learning frameworks, including advanced competency in one of PyTorch, JAX, TensorFlow, or equivalent platforms for model development and optimization

About Inworld Inworld is a product-oriented research lab of top AI researchers and engineers, developing best-in-class realtime multimodal models and the only realtime orchestration platform optimized for thousands of queries per second. We’ve raised more than $125M from Lightspeed, Section 32, Kleiner Perkins, Microsoft’s M12 venture fund, Founders Fund, Meta and Stanford, among others. Our technology has powered experiences from companies such as NVIDIA, Microsoft Xbox, Niantic, Logitech Streamlabs, Wishroll, Little Umbrella and Bible Chat. We’ve also been recognized by CB Insights as one of the 100 most promising AI companies globally and have been named one of LinkedIn's Top 10 Startups in the USA. Who We're Looking For A year ago, reliably working agentic systems and sub-second multimodal inference at scale barely existed. Nobody has a decade of experience here. So we're not screening for a resume template — we're looking for strong people from varied backgrounds who learn fast, thrive in ambiguity, and can show us what they've built, broken, and understood. Experience We Find Useful You don't need all of this. But you need enough to make a case. • Inference Optimization. Deep understanding of modern serving frameworks and techniques like vLLM or TRT-LLM., • Model Acceleration. Hands-on experience with quantization, distillation, caching strategies , continuous batching, paged attention, and speculative decoding., • High-Performance Systems. Proficiency in C++, CUDA, Rust, or highly optimized Python. You know how to profile code and squeeze every ounce of performance out of NVIDIA GPUs., • Distributed Systems & Scaling. Experience with Kubernetes, Ray, custom load balancing, multi-GPU/multi-node inference, and reliably handling thousands of concurrent connections., • Public work. Non-trivial systems programming projects, open-source contributions to major inference engines, or deep-dive technical write-ups., • Full-cycle ownership. You can take a model from the research team, containerize it, optimize its serving, and ensure it runs reliably in production., • Background. PhD in CS, Physics, Math, or equivalent practical experience building backend or ML systems. Who Thrives Here • You don’t need a roadmap to start walking; you’re comfortable picking a direction and building the map as you go., • You believe engineering isn't finished until it’s shipped and stable. You have a bias for impact over purely theoretical optimizations., • You don't just ship code; you obsess over the why. You’re the first to question an architecture if you think there’s a better way to solve the core latency or throughput problem., • You aren't satisfied with "the PM said so." You thrive on deep context and want to understand the fundamental logic behind every decision we make. What Working Here Is Like We hand you unclear problems and expect you to make them clear. We value engineers who say "I don't know yet" and then design the benchmark or prototype that finds out. We treat performance, latency, and reliability as first-class product features, not a box to check before launch. Impact comes before everything else, though we support sharing work and open-source contributions that move the field forward. Your work should be visible. Flat structure, fast iterations, minimal process theater. The base salary range for this full-time position is £140,000 – £200,000. In addition to base pay, total compensation includes equity and benefits. Within the range, individual pay is determined by work location, level, and additional factors, including competencies, experience, and business needs. The base pay range is subject to change and may be modified in the future. Candidates must already have the legal right to work in the United Kingdom, as visa sponsorship is not available for this role. For candidates interested in relocating to the San Francisco Bay Area in the future, full U.S. visa and relocation support may be available, subject to business needs and applicable legal and work authorization requirements.

About Us: Based in San Francisco, California, Turing is the world’s leading research accelerator for frontier AI labs and a trusted partner for global enterprises deploying advanced AI systems. Turing supports customers in two ways: first, by accelerating frontier research with high-quality data, advanced training pipelines, plus top AI researchers who specialize in software engineering, logical reasoning, STEM, multilinguality, multimodality, and agents; and second, by applying that expertise to help enterprises transform AI from proof of concept into proprietary intelligence with systems that perform reliably, deliver measurable impact, and drive lasting results on the P&L. Ideal Background: This role is ideal for engineers who have built production systems at companies like Google, Microsoft, Apple, Amazon, Meta, or similar high-scale engineering organizations. We especially welcome graduates from leading programs such as Harvard, Columbia, Princeton, Yale, University of Pennsylvania, and comparable institutions — though exceptional experience and skill always take precedence over pedigree. Project Overview: As a Software Engineering evaluator, you will create cutting-edge datasets for training, benchmarking, and advancing large language models, collaborating closely with researchers. This includes curating code examples, providing precise solutions, and making corrections across the full stack — in Python for backend and ML workflows, and JavaScript (React, Node.js) for frontend and API layers, alongside C/C++, Java, Rust, and Go. You will evaluate and refine AI-generated code for efficiency, scalability, and reliability, and work with cross-functional teams to enhance enterprise-level AI-driven coding solutions. What Does a Typical Day Look Like? • Work on AI model training initiatives by curating code examples, building solutions, and correcting code across both Python and JavaScript (React, Node.js), with additional work in C/C++, Java, Rust, and Go., • Evaluate and refine AI-generated code across backend and frontend contexts to ensure that it is efficient, scalable, and reliable., • Collaborate with cross-functional teams to enhance AI-driven coding solutions against industry performance benchmarks., • Build agents that can verify the quality of the code and identify error patterns across full-stack applications., • Hypothesize on steps in the software engineering cycle (prototyping, architecture design, API design, production implementation, launch, experiments, monitoring, operational maintenance) and evaluate model capabilities on them., • Design verification mechanisms that can automatically verify a solution to a software engineering task. Required Skills: • Several years of software engineering experience (3 years or more), • Strong expertise in building full-stack applications using Python and JavaScript (React, Node.js), with the ability to work across backend and frontend codebases., • Experience deploying scalable, production-grade software using modern languages and tools., • Deep understanding of software architecture, design, development, debugging, and code quality/review assessment., • Excellent oral and written communication skills for clear, structured evaluation rationales. Engagement Details: • Commitment: flexible engagement, minimum 10 hrs/week, up to 40 hrs/week, • Type: Contractor (no medical/paid leave), • Duration: 1 month (potential extensions based on performance and fit), • Location: Candidates must be based in the United States Evaluation Process: • The application process takes 15–30 minutes., • Completion of an AI video interview is required. Note: As part of assessments you will go through an AI video interview. After applying, you will receive an email with a login link. Please use that link to access the portal and complete your profile. Know amazing talent? Refer them at ___, and earn money from your network.

Summer Placement: First-Year Optometry Students (6 Weeks) - Vision Express. Join one of our stores for a 6‑week summer placement and gain hands‑on optical retail experience with global brands such as Ray‑Ban, Oakley, Prada, Meta & more. Valuable insight ahead of a potential 2027 CLiP placement

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