Staff Machine Learning Engineer (Emergency Trajectory Models)

Join Wayve, a leading company in the autonomous vehicle industry. As a Staff Machine Learning Engineer, you will lead the development of a learned emergency trajectory model for high-consequence maneuvers. You will define the technical strategy, design and train trajectory-generating policies, build a data strategy, create evaluations, and lead integration into the driving stack. You will have the opportunity to make a significant impact on the future of self-driving cars.

• Lead the technical direction and delivery of a learned emergency trajectory model for low-frequency, high-consequence maneuvers.
• Set the technical strategy and roadmap for the emergency trajectory model, including its behavioral scope, operating envelope, system interfaces, and measurable acceptance criteria.
• Design and train trajectory-generating policies using the methods best supported by evidence, including behaviour cloning, reinforcement learning, or other sequential decision-making approaches.

• Deep expertise developing learned trajectory-generation or policy models for embodied systems, including architecture design, objective design, training, and empirical validation
• Strong machine learning engineering skills in Python and PyTorch, with experience building reproducible training and evaluation systems on large, heterogeneous datasets
• Hands-on experience with behaviour cloning, reinforcement learning, or related methods, including objective design, distribution shift, robustness, and closed-loop failure analysis
• A track record of staff-level technical leadership: setting direction for ambiguous machine learning programmes, aligning multiple teams, and carrying work from research through production deployment
• Exceptional technical judgement and communication: able to make safety-relevant trade-offs explicit, define the evidence needed for decisions, and lead without relying on formal authority
• We understand that everyone has a unique set of skills and experiences and that not everyone will meet all of the requirements listed above. If you’re passionate about self-driving cars and think you have what it takes to make a positive impact on the world, we encourage you to apply
• Experience applying learned models in autonomous driving or robotics, with strong understanding of motion planning, vehicle dynamics, control, or collision avoidance
• Experience with specialist, fallback, redundant, mixture-of-experts, or model-routing architectures and the interfaces used to select between them
• Experience mining, generating, or evaluating rare events using simulation and fleet or real-world data
• Experience deploying learned policies under real-time latency, reliability, and compute constraints; proficiency in C++, CUDA, or systems optimisation
• Experience with multimodal, transformer-based, diffusion-based, or other generative trajectory or policy models