Staff / Senior Machine Learning Engineer, Reinforcement Learning
Salary not listed • Sunnyvale, California USA • Full-time
Posted 25d ago
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Job Location
Sunnyvale, California USA
Tech Stack
Remote Work Policy
On-site
Employment Type
Full-time
Categories
Machine Learning Engineer
About the job
Wayve is building the leading AI platform for autonomous driving, pioneering an end-to-end AI approach that enables vehicles to learn directly from real-world experience. This role focuses on advancing reinforcement learning methods for end-to-end driving models, identifying opportunities where learning from reward or feedback can improve beyond behavior cloning. You will take promising ideas from design through large-scale experiments, rigorous evaluation, and integration into their best driving models, contributing to the development of embodied intelligence that moves real vehicles safely.
Responsibilities
- Shape and execute the reinforcement learning roadmap for Driving Core / Core Model Safety.
- Develop and evaluate post-behavior-cloning optimization methods, including offline and off-policy reinforcement learning.
- Improve reward models and related learning signals used to train and evaluate driving policies.
- 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 successful methods in the shared ML stack.
- Communicate decisions and results clearly.
- Raise the technical bar through design reviews, code reviews, and mentoring.
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: able to turn ambiguous 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 bring others along through clear communication.
- Experience with offline reinforcement learning, imitation learning, reward modeling, preference learning, or post-training of large neural policies (desirable).
- Experience in autonomous vehicles, robotics, control, or safety-critical physical systems (desirable).
- Experience with closed-loop simulation, off-policy evaluation, uncertainty or calibration, and evaluation under rare or shifted conditions (desirable).
- Experience training multimodal, transformer-based, or generative policy models at scale (desirable).
- Proficiency in C++, CUDA, distributed training, or performance optimization for production ML systems (desirable).