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Staff Tech Lead Manager, Machine Learning, Vision Models

Wayve · London, United Kingdom

Location
London, United Kingdom
Funding
$2.58B total
Posted
Oct 5, 2026

Wayve is hiring a Staff Tech Lead Manager, Machine Learning, Vision Models based in London, United Kingdom. Every apply link on Engg.space goes straight to the company's own careers page - no recruiter middleman, no generic job-board form.

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Role details

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 ENGINEERING TEAMS Wayve’s engineering teams are building the AI, robotics, simulation, data, and systems foundations needed to deploy a generalisable AI Driver safely and at scale. ABOUT THE MEASUREMENT TEAM The Measurement team within AI Evaluation builds and productionises the offline scene-understanding models that measure driving performance after on-road runs and in simulation. The team adapts on-vehicle and foundation-model technology to exploit the greater compute, model capacity, and temporal context available offline. 🧠 YOUR DAY-TO-DAY - Lead and develop a London-based team of Senior Machine Learning Engineers while remaining deeply engaged in technical direction. - Set the architecture for robust, scalable offline scene-understanding and measurement models. - Translate ambiguous business goals into sprint, quarterly, annual, and multi-year technical roadmaps. - Review designs and code, raise the engineering quality bar, and ensure models are reliable enough for customer-facing deliverables. - Align roadmaps across on-vehicle modelling, foundation models, Evaluation, Simulation, and Model Development Platform teams in the UK and US. - Identify capability gaps ahead of demand and build the technical and organisational case to address them. 🧩 WHAT YOU’LL BE WORKING ON - Offline models that predict and explain counterfactual driving outcomes. - Scene-understanding systems that assess coverage, mine rare events, and measure learned driving behaviour. - Rig-agnostic architectures built from transformer, multimodal, and foundation-model technology. - Production ML systems that shorten the loop between driving-model changes and reliable evaluation evidence. - A growing senior team operating across multiple organisations and locations. 🙌 YOU SHOULD APPLY IF - You have 8+ years in ML engineering, including hands-on computer vision with camera or lidar data and a record of shipping production ML systems. - You have 2+ years managing or tech-leading senior engineers and enjoy combining people leadership with technical leadership. - You bring staff-level depth in transformers, multimodal systems, foundation models, and large-scale training. - You are proficient in Python and PyTorch or a similar framework and have strong judgement about production-grade ML. - You can align roadmaps across teams and geographies and communicate technical strategy clearly to senior stakeholders. - Experience with 3D scene understanding, offline models, simulation, counterfactual evaluation, or distributed teams is beneficial. 🌱 Not ticking every box? That’s totally okay! If you’re passionate about autonomy and keen to learn, we encourage you to apply even if you don’t meet every requirement. MORE ABOUT WAYVE 🚀 Wayve is building the leading AI platform for autonomous driving. We are pioneering an end-to-end AI approach that enables vehicles to learn directly from real-world experience, developing the ability to adapt, generalise and improve at scale. Our ambition is to make autonomy universal. Wayve’s mapless and hardware-agnostic AI platform integrates with global OEM partners, enabling continuous software evolution from L2+ through to L4. HOW WE WORK 💻 — LOCATIONS & FLEXIBLE WORKING This role is based in London. We operate a hybrid model combining in-person collaboration in our offices and workshops with focused time working remotely. 🔍 THE INTERVIEW PROCESS - Initial call / recruiter screen (25 min) - Hiring Manager Domain Chat (60 min) - Deep-dive interviews covering ML architecture, systems, and domain expertise (4 hours) - Final interview focused on mission and values alignment (45 min) We’ll always explain the format and work around your availability. WHAT’S IN IT FOR YOU (LOCATION DEPENDENT) - 💰 Salaries benchmarked against the market annually - 📈 Meaningful equity - ✈️ Relocation support and visa sponsorship where applicable - ✅ Hybrid working and access to vehicle workshops and labs - 📚 Learning and development support - 🩺 Comprehensive location-dependent health, family, pension, and wellbeing benefits 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. At Wayve we're committed to creating a diverse, fair and respectful culture that is inclusive of everyone based on their unique skills and perspectives, and regardless of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, veteran status, pregnancy or related condition (including breastfeeding) or any other basis as protected by applicable law. For more information visit Careers at Wayve. https://wayve.ai/careers/ To learn more about what drives us, visit Values at Wayve https://wayve.ai/careers/ DISCLAIMER: We will not ask about marriage or pregnancy, care responsibilities or disabilities in any of our job adverts or interviews. However, we do look to capture information about care responsibilities, and disabilities among other diversity information as part of an optional DEI Monitoring form to help us identify areas of improvement in our hiring process and ensure that the process is inclusive and non-discriminatory.

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