Staff / Senior Machine Learning Engineer, Reinforcement Learning

What you'll do

• Shape and execute the reinforcement learning roadmap, with measurable behavioral goals.
• Develop and evaluate reward-guided and post-behavior-cloning methods, training workflows, and reward models.
• Define evaluation evidence and productionize successful methods; provide technical leadership through reviews and mentoring.

What they ask for

• Strong track record developing and experimentally validating reinforcement learning or related sequential decision-making methods on complex, high-dimensional problems.
• Deep understanding of reinforcement learning fundamentals, including policy and value learning, off-policy learning, function approximation, distribution shift, and learned-objective failure modes.
• Hands-on experience with behaviour cloning, reinforcement learning, or related methods.
• Proficiency in Python and PyTorch, strong software engineering practices, and experience building reliable ML training and evaluation systems.

Nice to have

• Experience with offline reinforcement learning, imitation learning, reward modeling, preference learning, or post-training large neural policies.
• Experience in autonomous vehicles, robotics, control, or other safety-critical physical systems.

Tools

Python, PyTorch, C++, CUDA

AI focus

Develop reinforcement-learning methods and reward-guided optimization for end-to-end autonomous-driving policies, including a learned emergency trajectory model and evaluation for safe deployment.