Tech Lead, Machine Learning (Applied AI)

Tech Lead, Machine Learning (Applied AI) Over 5 billion people use basic applications today, email, notes, tasks, calendar, and none of them are truly AI native. Our client's mission is to build proactive applications for anyone in the world, including people who aren't used to complex prompting. They've just raised $100million and are bringing intelligence to conversations, errands, organising and workflows, with minimal to no prompting required. The product is focused on achieving high reliability for long running workflows, persistent context, and real world task completion, with a strong belief that products should dramatically reduce hallucinations. The broader objective is to help anyone organise their life, so they can spend time on what's valuable and meaningful. As Tech Lead, Machine Learning, you'll own the execution layer of the company's intelligence, turning research and model capabilities into reliable, scalable production systems. You'll work across the full model lifecycle, data, training, evaluation, inference, and deployment. This is a hands on leadership role for someone who wants to operate at the intersection of research, systems, and product. What You'll OwnOwn the end to end ML systems powering the company, from data and training through to evaluation, inference, and deploymentBuild and evolve training and fine tuning pipelines for large modelsDesign evaluation systems that measure capability, robustness, safety, and real world product performanceArchitect high performance inference systems, optimising latency, GPU utilisation, memory, cost, and reliabilityBuild data pipelines and systems for high quality real world and synthetic training dataEstablish reliable production infrastructure for deploying, monitoring, and continuously improving modelsPartner closely with research and application engineering to turn model capabilities into product improvementsMake pragmatic technical trade offs and rapidly iterate based on real world performance What They're Looking ForExperience building and shipping ML systems used in production, not just research prototypesStrong understanding of modern large model training, fine tuning, evaluation, and inferenceStrong software engineering and systems fundamentalsExperience operating ML workloads at meaningful scale, particularly GPU based systemsStrong technical judgment and the ability to navigate ambiguous problems independentlyA bias toward experimentation, measurement, and shippingHigh standards for correctness, reliability, and production qualityExperience with API design (REST/gRPC)Proficiency in backend languages such as Python, Node, or GoComputer Science or equivalent degree OutcomesResearch and models reliably translate into production ready solutions with clear performance and quality targetsML pipelines, training loops, and inference systems are stable, efficient, and maintainableProduction issues are detected, debugged, and resolved quickly, minimising user impactTeam members are supported, aligned, and able to deliver high impact ML work with minimal frictionIterations on models and systems are measurable, safe, and improve user experience over time Tech StackPythonPyTorch / JAXGPU based training and inference systems How They WorkA small, high talent density, hands on team. Engineers have broad ownership and are expected to exercise strong judgment and execute independently. Decisions are made quickly, the team works closely together, and speed is balanced with strong engineering fundamentals. Less process, more building something exceptional. Please apply now with your latest cv for consideration or message me directly.