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Wayve is building an AI platform for autonomous driving using end-to-end learning from real-world experience. As a Staff Machine Learning Engineer specializing in Reinforcement Learning, you will be a technical owner for RL methods within the Driving Core and Core Model Safety teams, working across AV Core, Simulation, Evaluation, and Product Engineering.
Your responsibilities include:
- 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 and other reward-guided approaches; design regularization, data strategy, and diagnostics to make policies reliably better.
- Improve reward models and learning signals used to train and evaluate driving policies, working with partner teams to strengthen 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 to 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.
You'll work in a hybrid model across Wayve's main hubs (London, Sunnyvale, Yokohama, Herzliya, Vancouver, Leonberg), with core hours and hands-on access to vehicle workshops and labs.
REQUIREMENTS:
Essential:
- Strong track record developing and experimentally validating reinforcement learning or closely related sequential decision-making methods on complex, high-dimensional problems.
- Deep understanding of modern reinforcement learning fundamentals, including policy and value learning, off-policy learning, function approximation, distribution shift, and failure modes of learned objectives.
- Hands-on experience with behavior cloning, reinforcement learning, or related methods.
- Proficiency in Python and PyTorch with strong software engineering practices and hands-on experience building reliable machine learning training and evaluation systems.
- Excellent experimental judgment: able to turn ambiguous behavioral problems into falsifiable hypotheses, useful metrics, disciplined ablations, and clear technical decisions.
- Senior-level ownership and collaboration: able to lead a substantial technical area, work across research and engineering boundaries, and communicate clearly in writing and verbally.
Desirable:
- Experience with offline reinforcement learning, imitation learning, reward modeling, preference learning, or post-training of large neural policies.
- Experience in autonomous vehicles, robotics, control, or safety-critical physical systems, including understanding of motion planning, vehicle dynamics, control, or collision avoidance.
- Experience with closed-loop simulation, off-policy evaluation, uncertainty/calibration, and evaluation under rare or shifted conditions.
- Experience training multimodal, transformer-based, or generative policy models at scale.
- Proficiency in C++, CUDA, distributed training, or performance optimization for production ML systems.
