Senior Algorithm Engineer, Reinforcement Learning

What you'll do

• Train conditioned policies to simulate driving behaviors and validate the autonomous-driving stack.
• Develop RL algorithms that prioritize safety constraints and collaborate on reward functions and evaluation metrics.
• Improve multi-agent training pipelines and neural architectures, and integrate models with Simulation and Planning teams.

What they ask for

• Proven experience training and deploying deep RL algorithms for real-world robotic or autonomous systems.
• Expertise in Python and PyTorch, with knowledge of deep learning architectures and optimization.
• MS or PhD in Computer Science, Robotics, or a related quantitative field.
• Ability to diagnose RL training challenges, including variance management and distribution shift.

Nice to have

• Experience with constrained optimization or safety-critical learning frameworks.
• MARL experience, or familiarity with vehicle dynamics and behavior planning for long-haul highways.

Tools

Python, PyTorch, PPO, SAC

AI focus

Develops and trains deep reinforcement-learning and multi-agent policies for autonomous driving, simulation, and safety-critical behavior.