Staff / Senior Machine Learning Engineer, Reinforcement Learning

Overview

As a Senior/Staff ML Engineer in Wayve’s AV Core, you shape reinforcement learning methods to improve end-to-end driving models. You work across Driving Core and Core Model Safety to push from problem definition to deployment, delivering measurable improvements in driving behavior. You’ll design post-behavior-cloning optimization, evaluate policies, and build scalable training workflows. This is a high-impact role at a company actively translating real-world driving data into safe, learnable autonomy.

Pay / Benefits

• hybrid working model
• learning and development budgets
• equity
• relocation support and visa sponsorship
• health and dental insurance
• wellbeing programs and team socials

Responsibilities

• 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, reward-guided approaches) with robust data strategy and diagnostics
• Improve reward models and learning signals for driving policies in collaboration with partner teams
• Build robust training and experimentation workflows using large-scale driving data; diagnose distribution shift and bias
• Define evidence across offline metrics, open-loop tests, closed-loop simulation, and on-road evaluation
• Productionize methods in the ML stack; contribute to design/code reviews and mentoring

Key requirements

• Strong track record in reinforcement learning or sequential decision-making for high-dimensional problems
• Deep understanding of modern RL fundamentals (policy/value learning, off-policy learning, function approximation, distribution shift)
• Hands-on experience with behaviour cloning, RL, or related methods
• Proficiency in Python and PyTorch; solid software engineering practices for ML systems
• Excellent experimental judgment and ability to formulate falsifiable hypotheses and clear metrics
• Senior-level ownership and cross-team collaboration; clear written and verbal communication

• ownership
• collaboration
• experimental rigor
• Python
• PyTorch
• reinforcement learning

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