Senior Staff Deep Reinforcement Learning Engineer

What you'll do

• Formulate complex driving tasks as RL problems with well-shaped reward functions and expressive state representations.
• Design and train model-based deep RL agents using GPU-accelerated simulation at massive scale.
• Build and maintain distributed training infrastructure in JAX across large compute clusters.
• Develop agentic optimization systems to automate code improvement, experiment execution, and metric analysis.
• Manage the full lifecycle of RL policies from initial problem formulation to on-vehicle inference.
• Improve simulator software to enhance the training environment for reinforcement learning agents.
• Integrate learned components into the broader autonomy stack to produce robust, shippable vehicle behavior.

What we're looking for

• Hold a BS, MS, or PhD in CS, EE, Robotics, or a related field.
• Possess a strong foundation in reinforcement learning and deep learning.
• Demonstrate proficiency in JAX or similar functional ML frameworks including JIT compilation and vectorized environments.
• Have hands-on experience training RL agents at scale in robotics, autonomous driving, or real-time decision-making domains.
• Exhibit a deep grasp of core RL concepts like policy gradients, reward shaping, and sim-to-real transfer.
• Show proficiency in using AI coding tools throughout the full software development lifecycle.
• Maintain a data-driven mindset to build experiment pipelines and analyze training runs.