Senior Machine Learning Engineer, Driving Product

Wayve • Sunnyvale, CA

2d • $311,850 - $350,625 • Hybrid

About The Position

The Senior Machine Learning Engineer will develop and ship AI driver models that introduce and improve driving behaviours across Level 2, Level 3, and Level 4 autonomy. The Driving Product team takes models from data and training through evaluation and on-road validation, then deploys them in vehicles. The work supports commercial deliveries, including Nissan production and Robotaxi, and combines machine learning research with production-focused engineering.

The role owns key parts of the model training lifecycle, including data curation, evaluation strategy, success metrics, and iteration planning. It includes running and analysing on-road and offline experiments, and improving models through repeated training cycles. The engineer will work end-to-end across data, training, evaluation, deployment, and performance monitoring, building production-ready ML systems with strong quality and reliability practices.

The work involves models that predict vehicle trajectories and interact with vehicle controllers and actuators, as well as methods such as reinforcement learning, behavioural cloning, and distillation. The role may involve multi-sensor inputs such as camera, radar, and lidar. Wayve operates a hybrid working model, with hubs in London, Sunnyvale, Yokohama, Herzliya, Vancouver, and Leonberg.

Requirements

• Proven track record training deep learning models with clear end-to-end ownership of data, training, evaluation, and iteration.
• Experience taking ML models into production and working through real-world constraints, quality, and safety requirements.
• Strong Python skills and hands-on experience with modern ML frameworks.
• Comfort shipping production-facing ML systems, not only research or proof-of-concept work.
• Background in end-to-end autonomous driving/embodied AI or large-scale product ML systems such as recommendations or ranking.
• Experience with reinforcement learning or behavioural cloning, especially where it materially improved real-world performance.

Nice To Haves

• Familiarity with model compression and deploying ML to embedded systems.
• Experience working with multi-sensor inputs such as camera, radar, and lidar where relevant.

Responsibilities

• Develop AI driver model architectures and training algorithms to introduce and enhance driving behaviours for L2/L3/L4.
• Own key parts of the training model lifecycle, including evaluation strategy, success metrics, and iteration planning.
• Mine, bucket, and curate real-world and synthetic data to teach specific driving behaviours, and implement data schemes to support training.
• Run and analyse on-road and offline experiments, translate results into clear next steps, and drive improvements through repeated training cycles.
• Develop models that predict vehicle trajectories and interact directly with controllers and actuators.
• Work across data, model training, evaluation, deployment, and performance monitoring.
• Develop on-road and off-road metrics to diagnose issues and drive data-led model iterations.
• Build production-ready ML systems using strong software engineering, quality, and reliability practices.

Benefits

• Salaries benchmarked against the market annually.
• Meaningful equity.
• Relocation support and visa sponsorship where applicable.
• Hybrid working and core hours.
• Learning and development budgets, with support for training, conferences, and growth.
• Comprehensive benefits including health insurance, dental insurance, enhanced maternity and paternity leave, retirement or pension where applicable, access to therapists, and wellbeing partner discounts.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed