Lead Technical Program Manager, AI Platform

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.

The role

As the Lead Technical Program Manager for AI Platform at Wayve, you'll build and lead the technical program management function for our AI Platform org - the data and compute infrastructure, model-development workflow tooling, training technology, compute management, and embedded / inference optimisation that get Wayve's models trained, iterated, and deployed onto the vehicle. This is the platform the whole company builds on.

You'll be the delivery partner to engineering leadership across AI Platform, driving predictable, high-leverage delivery of the systems that determine how fast Wayve can train and ship models. You'll lead flagship programs yourself while supporting and coaching a small, high-impact TPM team. Your impact is measured in developer velocity, compute efficiency and cost, training and inference performance, and how reliably the platform lands. A successful TPM leader is a force multiplier - helping teams move faster, more effectively, and with purpose.

Key Responsibilities

• Lead the function: Build, coach, and grow a small TPM team; hire to fill gaps and raise the bar.

• Own platform delivery: Partner with AI Platform leadership on planning, prioritisation, and execution across the platform roadmap - data & compute infra, dev tooling, training tech, compute management, and embedded / inference.

• Trusted partner for Engineering: You are a leader in the org. You will maintain the partnership with Engineering leaders to land big outcomes, pushing teams to land impact with high leverage and hold leaders to account.

• Drive flagship programs directly: Data & compute infrastructure, model-development workflow and tooling, training technology, compute management, and embedded / inference optimisation.

• Establish scalable practices: Planning cadences, governance, KPIs and dashboards, escalations and operational reviews.

• Align cross-functionally: Work with ML / research, infrastructure, embedded / on-vehicle, and engineering teams (and cloud / vendor partners); manage dependencies, risk, and trade-offs.

• Connect to outcomes and represent AI Platform: Tie delivery to developer velocity, compute efficiency and cost, and training / inference performance, and represent AI Platform in company-level reviews.

About you

In order to set you up for success as a Lead Technical Program Manager, AI Platform at Wayve, we're looking for the following skills and experience.

Essential

• 8+ years in platform / infrastructure: Deep, hands-on experience delivering platform or infrastructure programs as a technical program manager - compute, ML infra, developer tooling, training or inference systems - including people and process leadership. You've built or scaled a program function and you get things done with ownership and a bias for action.

• People leadership: You build, coach, and grow high-performing teams, and raise the bar on the craft.

• Highly technical: Credible with engineers across ML infrastructure, compute, and embedded systems, even though you won't write production code. A genuinely technical TPM who can go deep with platform and systems engineers.

  • Strong understanding of Machine Learning, GPUs, and the training and inference of large (500M-20B+ parameter) models, plus the compute and orchestration that support them.

  • Embedded / on-vehicle systems experience - inference optimisation, model deployment to constrained edge / embedded compute, and the hardware-software trade-offs involved.

  • Familiar with compute management and ML platform tooling - e.g. Kubernetes, Ray, Flyte, Docker, cloud (Azure), Python - and model-development / experiment workflow tooling.

  • Proficient with AI Agents to accelerate execution: e.g. Cursor, Claude, Codex.

• Stays neutral and adapts under pressure: Effective in ambiguous, fast-moving environments; you flex your style and bring structure without slowing delivery.

• Systems thinking: You reason about how the parts of a complex platform / systems stack fit together, and how a change in one area ripples into others.

• Product-minded: You focus on outcomes and the people your programs serve - prioritising by impact and defining what good looks like, not just tracking activity.

• Exceptional cross-functional leadership: You align and influence across ML / research, infrastructure, embedded, and engineering without relying on authority.

• Strategic & business impact: You link platform investment to measurable outcomes - developer velocity, compute efficiency, cost, and model performance.

• Clear, data-driven communicator: You set direction with clarity and steer with the right metrics.

• Growth mindset: Open to feedback and always looking to improve.

Desirable

• Working experience with a large-scale ML / compute platform used by 100s of engineers and researchers.

• Embedded / edge inference, on-device model optimisation, or hardware-aware ML experience.

• Background in autonomous vehicles, robotics, or another large-scale ML / infrastructure program.

• An engineering or computer science degree, or experience working as an engineer.

This is a full-time role based in our office in London. At Wayve we want the best of all worlds, so we operate a hybrid working policy that combines time together in our offices and workshops to fuel innovation, culture, relationships and learning, with time spent working from home.

A quick, honest note before you apply.

Wayve is not a mature, fully-structured place with the playbook already written. Much of how we work is still being written, and if you join, you’ll help write it. That suits people who want real ownership more than people who need a settled structure from day one.

If that sounds like the kind of problem you want to spend your time on, we’d really like to hear from you.