Staff / Senior Machine Learning Engineer, Reinforcement Learning

Wayve • Sunnyvale, CA

19h • Hybrid

About The Position

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 will 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 Wayve’s best driving models.

The role may support the Driving Core team, which develops learning methods that turn diverse driving data into robust closed-loop behavior, or the Core Model Safety team, which develops core model competencies for safe, driverless operation. For Driving Core, you will be a technical owner for reinforcement learning, working with researchers and engineers across AV Core, Simulation, Evaluation, and Product Engineering. For Core Model Safety, you will lead the technical direction and delivery of a learned emergency trajectory model for rare, high-consequence manoeuvres such as evasive steering and emergency braking, from problem definition through modelling, evaluation, integration, and deployment evidence.

The role involves shaping and executing the reinforcement learning roadmap, developing post-behavior-cloning optimization methods, improving reward models and learning signals, and building robust training and experimentation workflows using large-scale driving data. You will define evidence across offline metrics, open-loop tests, closed-loop simulation, and on-road evaluation, and help productionize successful methods in the shared ML stack. You will communicate technical decisions and results, contribute to design and code reviews, and mentor others.

Wayve operates a hybrid working model, combining in-person collaboration in its office hubs with remote focused work. The role offers the opportunity to work hands-on in vehicle workshops and labs. Location and benefits are dependent on location.

Requirements

• 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.
• Strong software engineering practices and hands-on experience building reliable machine learning training and evaluation systems.
• Excellent experimental judgment, including turning ambiguous behavioral problems into falsifiable hypotheses, useful metrics, disciplined ablations, and clear technical decisions.
• Senior-level ownership and collaboration skills, including leading a substantial technical area, working across research and engineering boundaries, and communicating clearly in writing and verbally.

Nice To Haves

• 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 another domain involving safety-critical physical systems.
• Understanding of motion planning, vehicle dynamics, control, or collision avoidance.
• Experience with closed-loop simulation, off-policy evaluation, uncertainty or 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 machine learning systems.

Responsibilities

• Shape and execute the reinforcement learning roadmap for Driving Core / Core Model Safety, selecting problems and methods based on 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 the regularization, data strategy, and diagnostics needed to make learned policies reliably better.
• Improve reward models and related learning signals used to train and evaluate driving policies in collaboration with partner teams.
• 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; 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.
• For the Core Model Safety work, lead technical direction and delivery of a learned emergency trajectory model, from problem definition through modelling, evaluation, integration, and evidence for deployment.

Benefits

• Market-benchmarked salaries, reviewed annually.
• Meaningful equity.
• Relocation support and visa sponsorship where applicable.
• Hybrid working and core hours.
• Opportunity to work hands-on in vehicle workshops and labs.
• Learning and development budgets, with support for training, conferences, and growth.
• Comprehensive benefits including health insurance, dental insurance, enhanced maternity and paternity leave, retirement or pension where applicable, access to therapists, wellbeing partnerships, and team socials.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed