Staff / Senior Machine Learning Engineer, Reinforcement Learning

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.

🛠️ About our Engineering Teams

The Driving Core team develops the learning methods that turn diverse driving data into robust closed-loop behavior. You'll be a technical owner for reinforcement learning within the group, working closely with researchers and engineers across AV Core, Simulation, Evaluation, and Product Engineering. Success means producing measurable improvements in driving behavior.

The Core Model Safety team develops the core model competencies that enable safe, driverless operation. In this team, you'll lead the technical direction and delivery of a learned emergency trajectory model for low-frequency, high-consequence manoeuvres such as evasive steering and emergency braking, taking the programme from problem definition through modelling, evaluation, integration, and evidence for deployment.

🧠 Your day-to-day

As a Senior / Staff Machine Learning Engineer in Wayve's AV Core organisation, you will advance reinforcement learning methods for end-to-end driving models. You'll identify where learning from reward or feedback can improve beyond behavior cloning, then take promising ideas from design through large-scale experiments, rigorous evaluation, and integration into our best driving models.

🧩 What you’ll be working on

• Shape and execute the reinforcement learning roadmap for Driving Core / Core Model Safety, selecting problems and methods against clear behavioral gaps and measurable success criteria.

• Develop and evaluate post-behavior-cloning optimization methods, including offline and off-policy reinforcement learning as well as other reward-guided approaches; design the regularization, data strategy, and diagnostics needed to make policies reliably better.

• Help improve the reward models and related learning signals used to train and evaluate driving policies, working with partner teams to strengthen their quality, scalability, and downstream usefulness.

• Build robust training and experimentation workflows using large-scale driving data; diagnose distribution shift, objective misspecification, optimization instability, and data or evaluation bias.

• Define evidence across offline metrics, open-loop tests, closed-loop simulation, and on-road evaluation, and distinguish genuine policy improvement from benchmark overfitting.

• Productionize successful methods in the shared ML stack, communicate decisions and results clearly, and raise the technical bar through design reviews, code reviews, and mentoring.