Lead Technical Program Manager, AI Platform

Job description

Before the detail, here's the challenge you'd help us solve. We build the embodied intelligence that moves real vehicles safely, and the ecosystem a billion machines will run on in the future. Very few people in AI can say this. Every role here, whatever the team, plugs into that. Here’s what this particular role covers. 🛠️ ABOUT OUR PROGRAM MANAGEMENT TEAM Wayve’s Program Management team turns complex technical goals into coordinated delivery. Working closely with engineering, research and product teams, we align priorities, manage dependencies and bring clarity to ambiguous challenges. We combine technical depth with cross-functional leadership to help teams move faster, more effectively and with purpose—focusing on meaningful outcomes rather than process for its own sake. As part of this team, you’ll build and lead the technical programme management function supporting our AI Platform organisation. AI Platform builds the data and compute infrastructure, model-development workflow tooling, training technology, compute management, and embedded and inference optimisation that enable Wayve’s models to be trained, iterated and deployed onto the vehicle. The systems this organisation delivers determine how quickly Wayve can develop and ship models, how efficiently we use compute, and how well our models perform in training and on the vehicle. 🧠 Your day-to-day - Build, coach and support a small, high-impact TPM team, helping people develop their skills, work through challenges and raise the standard of programme delivery. - Act as a trusted delivery partner to AI Platform and Engineering leadership, aligning priorities, challenging trade-offs and holding teams accountable for meaningful outcomes. - Work across ML and research, infrastructure, embedded and on-vehicle engineering teams, as well as cloud and vendor partners, to manage dependencies, surface risks and remove blockers. - Use KPIs, dashboards and operational reviews to understand delivery progress, identify bottlenecks and steer decisions with the right data. - Represent AI Platform in company-level reviews, communicating progress, risks and decisions clearly while connecting delivery to business impact. 🧩 What you’ll be working on: - Building and scaling the technical programme management function for AI Platform, hiring to fill capability gaps and growing a high-performing team. - Owning planning, prioritisation and execution across the AI Platform roadmap in partnership with engineering leadership. - Leading flagship programmes across data and compute infrastructure, developer tooling, training technology, compute management, model-development workflows, and embedded and inference optimisation. - Establishing scalable planning cadences, governance, escalation paths and operational practices that bring structure without slowing delivery. - Driving measurable improvements in developer velocity, compute efficiency and cost, training and inference performance, and platform reliability. - Helping engineering leaders deliver high-leverage outcomes, balancing technical trade-offs and ensuring commitments translate into impact. 🙌 You should apply if: - You bring 8+ years of hands-on technical programme management experience across platform, infrastructure, compute, ML infrastructure, developer tooling, training or inference systems. You have built or scaled a programme function, led people and processes, and get things done with ownership and a bias for action. - You build, coach and grow high-performing teams. You know how to develop people, hire to strengthen the team and raise the bar on technical programme management. - You have strong technical depth across ML infrastructure, compute and embedded systems. You can engage credibly with platform and systems engineers without being expected to write production code. Your experience includes: - A strong understanding of machine learning, GPUs, and training and inference for models from 500M to 20B+ parameters, including the compute and orchestration that support them. - Embedded or on-vehicle systems experience, including inference optimisation, deploying models to constrained edge compute, and hardware-software trade-offs. - Familiarity with compute management, ML platform tooling and model-development or experiment workflows, using technologies such as Kubernetes, Ray, Flyte, Docker, Azure and Python. - Proficiency with AI agents and coding assistants such as Cursor, Claude or Codex to accelerate execution. - You stay objective under pressure and adapt your approach in ambiguous, fast-moving environments. You bring structure and clarity without creating unnecessary process or slowing delivery. - You think in systems, understanding how the parts of a complex platform stack fit together and how changes in one area affect others. - You’re product-minded, focusing on the people your programmes serve, prioritising by impact and defin