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Wayve is building the leading AI platform for autonomous driving, pioneering an end-to-end AI approach that enables vehicles to learn directly from real-world experience. The company is seeking a Technical Program Manager to build and lead the technical programme management function supporting the AI Platform organisation.
AI Platform delivers 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 vehicles. This role is critical to how quickly Wayve can develop and ship models, how efficiently compute is used, and how well models perform in training and on the vehicle.
Day-to-day responsibilities include:
- Building, coaching and supporting a small, high-impact TPM team, developing their skills and raising the standard of programme delivery
- Acting as a trusted delivery partner to AI Platform and Engineering leadership, aligning priorities, challenging trade-offs and holding teams accountable for outcomes
- Working 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
- Using KPIs, dashboards and operational reviews to understand delivery progress, identify bottlenecks and steer decisions with data
- Representing AI Platform in company-level reviews, communicating progress, risks and decisions while connecting delivery to business impact
Key projects include:
- Building and scaling the technical programme management function, 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
- 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
Wayve operates a hybrid working model with main hubs in London, Sunnyvale, Yokohama, Herzliya, Vancouver and Leonberg, combining in-person collaboration with focused remote work.
REQUIREMENTS:
- 8+ years of hands-on technical programme management experience across platform, infrastructure, compute, ML infrastructure, developer tooling, training or inference systems
- Proven track record building or scaling a programme function, leading people and processes, with ownership and bias for action
- Demonstrated ability to build, coach and grow high-performing teams, developing people and raising the bar on technical programme management
- Strong technical depth across ML infrastructure, compute and embedded systems, able to engage credibly with platform and systems engineers without writing production code
- Deep understanding of machine learning, GPUs, and training and inference for models from 500M to 20B+ parameters, including compute and orchestration
- 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
- Ability to stay objective under pressure and adapt approach in ambiguous, fast-moving environments
- Systems thinking, understanding how complex platform stack components fit together and how changes affect others
- Product-minded approach, focusing on people served by programmes, prioritising by impact and defining what good looks like
- Ability to align and influence across ML and research, infrastructure, embedded and engineering teams without relying on formal authority
- Ability to connect technical investment to measurable outcomes including developer velocity, compute efficiency, cost and model performance
- Clear communication of direction and progress using appropriate metrics
- Openness to feedback and continuous improvement
IDEAL ADDITIONS:
- Experience working with large-scale ML or compute platforms used by hundreds of engineers and researchers
- Experience with embedded or edge inference, on-device model optimisation, hardware-aware ML, autonomous vehicles, robotics or large-scale ML and infrastructure environments
- Engineering or computer science degree, or experience working as an engineer
