Staff Tech Lead Manager (Machine Learning, Vision Models)

• As a Staff Tech Lead Manager on Wayve’s Measurement team in AI Evaluation, based in our London office, you will lead a team building and productionising offline scene understanding models
• You will directly manage four Senior Machine Learning Engineers and own the technical direction for turning technology from our on-vehicle models and Wayve Foundation Models into the robust, scalable scene understanding models that power our validation machine
• You should be motivated by measurement and evaluation as a first-class engineering discipline
• Your team’s mission is to predict and explain counterfactual outcomes, answering the question “what would have happened if we’d used a different driving model?”, and to build the models that let Wayve understand coverage, mine rare events, and assess the behaviour of our end-to-end AV2.0 driver after on-road runs and in simulation
• The offline environment gives the team headroom the vehicle never has: more compute per frame, larger models, and access to both past and future temporal context
• The outputs are mission-critical, directly informing model development decisions and customer deliverables
• This work currently spans several teams and sites, and you will pull it together into one coherent technical direction, working closely with on-vehicle modelling in AV Core, foundation model teams, Evaluation, simulation, and Model Development Platform across the UK and US
• Lead and grow the team - directly manage four Senior MLEs in London; hire complementary talent across MLE, SWE, and data science profiles as scope grows; develop strong senior ICs and hold a high bar
• Own the technical direction - guide the architecture for adapting shared on-vehicle models and Wayve Foundation Models for offline measurement use, exploiting the advantages of the offline environment: higher compute budgets, larger model capacity, and access to past and future temporal context
• Own the roadmap - drive planning at sprint, quarterly, and annual cadences; translate ambiguous business goals into concrete technical programmes; be equally comfortable setting multi-year direction and getting into the weeds of a sprint review
• Drive production quality - build rigorous engineering practice for ML systems relied on for customer-facing deliverables; champion rig-agnostic, generalisable architectures with clean interfaces; hold the line on architectural quality in a fast-moving environment
• Accelerate the development loop - ensure your team’s models reduce the time between a driving-model iteration and reliable, actionable feedback; make the whole AV development loop faster
• Partner and anticipate - align roadmaps with on-vehicle modelling and Evaluation teams across the UK and US; resolve technical conflicts at the right level; identify capability gaps 6-24 months out and build the case for investment to close them

Benefits

• Private healthcare: Choose our optional health insurance for comprehensive coverage for you and your family.
• Paid time off: Paid vacation plus public holidays and additional leave programs, ensuring you have time to unwind.
• Mental health resources: Through Spill, you can access therapy and mental health support.
• Community and socials: Join clubs or attend team socials to connect over hobbies, sports, or just for fun.
• Competitive compensation: Our compensation package includes cash and equity, making you a true partner in our success.
• Learning and development: Budgets for books, courses, and company-wide training to support your continuous growth.- Experience leading or partnering with distributed teams across UK and US time zones
• MS or PhD in Computer Science, Engineering, or a related field
• 2+ years line-managing or tech-leading senior engineers, including hiring, developing, and retaining strong ICs, with the appetite to keep doing both halves of the TLM role
• 8+ years in ML engineering, including hands-on computer vision with camera and/or lidar sensor data, and a track record of shipping production ML systems from research through to reliable, monitored, customer-facing software
• Proficient in Python and ML frameworks (esp. PyTorch), with strong judgement about what production-grade looks like for ML systems
• Desirable
• Staff-level technical depth that earns the trust of senior MLEs: transformer-based and multimodal architectures, foundation models, and large-scale training, with the ability to review designs and code credibly
• Knowledge of perception systems and 3D scene understanding for autonomy, such as cuboid detection, lane estimation, depth estimation, and large-scale semantic enrichment of driving scenes
• Strong cross-functional leadership: aligning roadmaps across teams and geographies, and communicating technical context and strategic direction clearly to engineers and senior leadership
• Familiarity with simulation or counterfactual evaluation methods for autonomous systems
• Experience with offboard or offline models: auto-labelling, model distillation, temporal or world models, or other ways of exploiting compute that on-vehicle systems cannot
• We understand that everyone has a unique set of skills and experiences and that not everyone will meet all of the requirements listed above. If you’re passionate about self-driving cars and think you have what it takes to make a positive impact on the world, we encourage you to apply