Machine Learning Engineer

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 Machine Learning Engineer on Wayve's Measurement team in AI Evaluation, based in our Sunnyvale office, you will build the computer vision and scene understanding models Wayve uses to measure the performance of the Wayve Driver offline. You will adapt technology from our on-vehicle models and Wayve Foundation Models into offline models that understand coverage, mine rare events, and assess driving behaviour, and you will drive their accuracy and generalisation across vehicles, markets, and conditions. Measuring your own models rigorously is part of the work. You will define ground truth and correctness criteria across a complex driving taxonomy, and turn them into automated benchmarks and evidence that our validation pipelines and safety cases can stand on.

The Measurement team builds and qualifies the scene understanding models Wayve uses to measure driving performance offline, after on-road runs and in simulation. Offline is where the interesting headroom is: more compute per frame, larger foundation models, and access to both past and future temporal context that the vehicle never has. The outputs are mission-critical, directly informing model development decisions and customer deliverables. You will work in a focused, high-impact senior team with strong ownership, access to fleet-scale camera, lidar, and simulation data, and close partners across on-vehicle modelling, evaluation, data curation, and simulation.

Key responsibilities

• Develop the models - build, train, and fine-tune the scene understanding models at the centre of Wayve's offline measurement, adapting on-vehicle architectures and Wayve Foundation Models for offline use.
• Drive accuracy and generalisation - improve model performance across vehicle platforms, geographies, and driving conditions; diagnose failure modes and close the loop on blind spots.
• Exploit the offline environment - use the advantages the vehicle does not have: higher compute budgets, larger model capacity, bidirectional temporal context, and multi-task or joint representation learning.
• Measure what you build - benchmark your models, set quality bars, and use metrics and error analysis to steer the next iteration; treat measurement as the feedback that drives the modelling.
• Make the evidence credible - ensure benchmarked results are statistically defensible and fit to feed validation pipelines at scale and our broader safety cases, across the product portfolio.
• Align priorities and mentor - work day-to-day with on-vehicle modelling, evaluation, data curation, and simulation teams across sites; raise the bar on engineering and modelling practice; mentor others on the team; keep sight of division and company priorities and how Measurement work enables them.

About you

In order to set you up for success as a Staff Machine Learning Engineer at Wayve, we’re looking for the following skills and experience.

Essential

• 5+ years in ML engineering, including training and shipping deep learning models in production, with pathfinding in ambiguous modelling problems from scoping through to a direction others build on.
• Hands-on experience training modern computer vision models, including transformer-based and multimodal or VLM architectures for detec