Staff Machine Learning Engineer, Robotics

Wayve • Sunnyvale, CA

1d • Hybrid

About The Position

Wayve is hiring a hands-on Machine Learning Engineer with a strong robotics background for its Vehicle Generalization team. The team makes Wayve’s autonomous driving stack efficient, scalable, and predictable to deploy across vehicle platforms. It improves how AI models generalize to changes in perception—including cameras, camera intrinsics, field of view, lens distortion, and calibration—and embodiment, including vehicle dynamics, actuation limits, and rig geometry. The team also owns the Generalization Benchmark, which measures how models transfer across platforms, and its work supports production programmes with major OEMs.

In this role, you will identify where models fail across platforms, determine why, address failures through training, and validate improvements with rigorous evaluation. You will develop cross-platform model approaches, benchmarks, and metrics; investigate real-world driving issues; and collaborate with controls, calibration, OEM, Driving Core, and application engineering teams to bring research into production. You will work with real-world multi-sensor driving data and support customer programmes, including some travel. You will also mentor teammates and help raise the team’s ML engineering standards.

The role is based in Sunnyvale, California (preferred), or London. Wayve uses a hybrid working model, combining in-person collaboration with remote focused work. The position offers meaningful ownership in a company whose processes and ways of working are still evolving.

Requirements

• Several years of experience building and shipping ML systems in PyTorch and Python, including training at scale, debugging experiments, and rigorous evaluation.
• Strong robotics or automotive background in areas such as state estimation, sensor fusion, calibration, vehicle dynamics, or controls.
• Systems perspective and ability to trace problems through sensors, data, models, and actuation.
• Experience working with real-world, multi-sensor driving or robot data.
• Clear communication across teams and comfort owning ambiguous problems from start to finish.

Nice To Haves

• Experience with end-to-end or learned driving and planning policies, imitation learning, or reinforcement learning.
• Experience with platform bring-up or deployment across multiple vehicles.
• C++ skills and experience with on-vehicle or embedded deployment.

Responsibilities

• Design and train models that are robust to differences between vehicles, including through conditioning or augmentation for vehicle kinematics and camera parameters.
• Create benchmarks and metrics for cross-platform performance and translate results into clear decisions for model and platform teams.
• Help identify root causes of on-road issues such as late turns, lane-keeping problems, and steering problems.
• Collaborate with controls, calibration, and OEM engineers to distinguish model problems from system problems and identify high-impact model improvements.
• Move research into production with Driving Core and application engineering teams.
• Support customer programmes, including some travel.
• Mentor teammates and help raise the bar for ML engineering across the team.

Benefits

• Meaningful equity
• Relocation support and visa sponsorship where applicable
• Hybrid working and access to vehicle workshops and labs
• Learning and development support
• Comprehensive location-dependent health, family, retirement, and wellbeing benefits

What This Job Offers

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