Staff / Senior Machine Learning Engineer, Reinforcement Learning

As a Senior / Staff ML Engineer in Wayve’s AV Core, you drive reinforcement learning efforts for end-to-end driving models and safety-critical trajectories. You will shape RL roadmaps, experiment with post-behavior-cloning methods, and improve learning signals across teams, contributing to robust, scalable driving policies. You’ll blend research with production-ready engineering to raise the bar in real-world autonomy. This role offers ownership across a core area and a chance to advance safe, generalisable driving intelligence.

• Equity with long-term ownership
• Relocation support and visa sponsorship
• Hybrid working with core hours
• Learning and development budgets
• Comprehensive health benefits
• Enhanced parental leave

• Shape and execute the reinforcement learning roadmap for Driving Core / Core Model Safety
• Develop and evaluate post-behavior-cloning optimization methods (offline/off-policy RL) and related data strategies
• Improve reward models and learning signals for driving policies with partner teams
• Build robust training and experimentation workflows using large-scale driving data
• Define evidence across offline metrics, open-loop tests, closed-loop simulation, and on-road evaluation
• Productionize methods in the shared ML stack and contribute to design/code reviews and mentoring

• Strong track record in developing and validating reinforcement learning or related sequential decision-making methods on high-dimensional problems
• Deep understanding of modern reinforcement learning fundamentals (policy/value learning, off-policy learning, function approximation, distribution shift)
• Hands-on experience with behaviour cloning, reinforcement learning, or related methods
• Proficient in Python and PyTorch with solid software engineering practices for ML systems
• Excellent experimental judgment and ability to translate ambiguity into falsifiable hypotheses and clear decisions
• Senior-level ownership and cross-functional collaboration across research and engineering

• ownership
• collaboration
• clear communication
• reinforcement learning
• offline RL
• imitation learning