Machine Learning Engineer, Robotics

Before the detail, here's the challenge you'd help us solve.

We build the embodied intelligence that moves real vehicles safely, and the ecosystem a billion machines will run on in the future. Very few people in AI can say this. Every role here, whatever the team, plugs into that.

Here's what this particular role covers.

Wayve's engineering teams are building the AI, robotics, simulation, data, and systems foundations needed to deploy a generalisable AI Driver safely and at scale.

Vehicle Generalization makes it efficient, scalable, and predictive to deploy Wayve's autonomous driving stack across a wide range of vehicle platforms by making our core autonomy generalise as widely as possible.

We work across pre- and post-training to improve how well our AI models generalise zero-shot to changes in perception—including cameras, intrinsics, field of view, lens distortion, and calibration—and embodiment, including vehicle dynamics, actuation limits, and rig geometry. We also own the Generalization Benchmark, Wayve's tool for measuring how models transfer across platforms. Our work goes into production programmes with major OEMs.

We're looking for a hands-on Machine Learning Engineer with a strong robotics background who understands how a real vehicle senses, estimates its state, and moves. You'll identify where our models break down across platforms, understand why, and address those failures in training—then prove the improvement through rigorous evaluation.

• 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.

• 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 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.

• 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.

Wayve is building the leading AI platform…