Staff Tech Lead Manager, Machine Learning, Vision Models

About us

Founded in 2017, Wayve is the leading developer of Embodied AI technology. Our advanced AI software and foundation models enable vehicles to perceive, understand, and navigate any complex environment, enhancing the usability and safety of automated driving systems. Our vision is to create autonomy that propels the world forward. Our intelligent, mapless, and hardware-agnostic AI products are designed for automakers, accelerating the transition from assisted to automated driving.

In our fast-paced environment big problems ignite us—we embrace uncertainty, leaning into complex challenges to unlock groundbreaking solutions. We aim high and stay humble in our pursuit of excellence, constantly learning and evolving as we pave the way for a smarter, safer future. At Wayve, your contributions matter. We value diversity, embrace new perspectives, and foster an inclusive work environment; we back each other to deliver impact. Make Wayve the experience that defines your career!

The role

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.

Key responsibilities

• 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.

About you

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

Essential

• 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 applications.