Staff Machine Learning Engineer, Robotics

🧠 Your day-to-day

• Design and train models that are robust to differences between vehicles, using approaches such as conditioning or augmentation for vehicle kinematics and camera parameters.

• Create benchmarks and metrics for cross-platform performance, translating results into clear decisions for model and platform teams.

• Help root-cause on-road issues such as late turns, lane-keeping, and steering problems. Work with controls, calibration, and OEM engineers to separate model problems from system problems and identify the highest-impact model improvements.

• Move research into production in collaboration with Driving Core and application engineering teams.

• Support customer programmes, with some travel.

• Mentor teammates and help raise the bar for ML engineering across the team.

🧩 What you’ll be working on

• Pre- and post-training methods that improve zero-shot transfer across vehicle platforms.

• Robustness to changes in camera configuration, calibration, vehicle dynamics, actuation limits, and rig geometry.

• The Generalization Benchmark and metrics that measure how models transfer between platforms.

• Real-world, multi-sensor driving data and the interaction between sensors, data, models, and actuation.

• Production deployment of Wayve’s autonomy stack with major OEM partners.

🙌 You should apply if

• You have several years of experience building and shipping ML systems in PyTorch and Python, including training at scale, debugging experiments, and rigorous evaluation.

• You have a strong robotics or automotive background in areas such as state estimation, sensor fusion, calibration, vehicle dynamics, or controls.

• You bring a systems perspective and can trace a problem through sensors, data, models, and actuation.

• You have experience working with real-world, multi-sensor driving or robot data.

• You communicate clearly across teams and are comfortable owning ambiguous problems from start to finish.

Nice to have

• 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.

🌱 Not ticking every box? That’s totally okay! If you’re passionate about autonomy and keen to learn, we encourage you to apply even if you don’t meet every requirement.