Senior Machine Learning Engineer, Wayve

• 4+ years in ML engineering, including training and shipping deep learning models in production, comfortable taking ambiguous modelling problems from scoping through to a working solution
• Hands-on experience training modern computer vision models, including transformer-based and multimodal or VLM architectures for detection, segmentation, classification, or scene understanding, on camera and/or lidar sensor data
• Experience adapting or fine-tuning large pretrained or foundation models, and training shared representations across multiple tasks or objectives (multi-stage or joint training), including real trade-offs across data and losses
• Proficient in Python and ML frameworks (esp. PyTorch), with a solid foundation in software engineering practices and comfort with large-scale training
• Strong ownership: research-literate and pragmatic, able to drive a significant modelling workstream with autonomy, collaborate across teams, and mentor less experienced engineers
• Able to measure your own models: comfortable defining and reading the metrics that show whether a model is genuinely improving
• 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

Desirable

• As a Senior 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
• 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; contribute to strong engineering and modelling practice; mentor others on the team; understand how the team's priorities connect to the wider division.