Senior/Staff Deep Reinforcement Learning Engineer Job at

DoorDash connects customers with local businesses for delivery, from restaurants to grocery, retail and its own DashMart stores. Our DD Labs team builds real-time autonomous delivery systems. The Planning & Decision-Making group is investing heavily in deep reinforcement learning to move beyond classical planning, learning policies that generalize across novel driving scenarios, handle long-tail edge cases, and improve continuously from large-scale fleet data.

Key responsibilities

• Formulate complex driving tasks as RL problems with well-shaped reward functions and expressive state/action representations
• Design and train model-based deep RL agents using GPU-accelerated simulation at massive scale, including improving the simulator itself
• Build and maintain distributed training infrastructure in JAX across large compute clusters
• Build agentic optimization systems that automatically improve code, run experiments, analyze metrics

Requirements

• BS/MS/PhD in CS, EE, Robotics, or a related field, with a strong foundation in reinforcement learning and deep learning
• Proficiency in using AI coding tools (e.g., Claude Code, Codex, Cursor) in the full software development lifecycle, including designing
• Hands-on experience training RL agents at scale, ideally in robotics, autonomous driving, or other real-time decision-making domains
• Proficiency in JAX or a similar functional ML framework; comfort with JIT compilation, vectorized environments, and GPU-accelerated simulation
• Policy gradients, value functions, exploration-exploitation, model-based RL, reward shaping, and sim-to-real transfer
• Comfortable building experiment pipelines, analyzing training runs, and letting metrics guide architectural decisions

Location and schedule

• Onsite in San Francisco, California

Salary and benefits

US base salary $168,000 to $247,000 a year. DoorDash also offers benefits; the full list is on the posting.

How to apply

Apply on the company's own site.