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Senior/Staff Deep Reinforcement Learning Engineer

22 hours, 35 minutes ago
United States
Full-time
Senior
Machine Learning Engineer
Software Development
Deep Learning Reinforcement Learning
Apply Now
DoorDash

DoorDash

DoorDash empowers small business owners by providing an affordable and convenient platform for local delivery services, primarily focusing on restaurant food delivery.

Air Freight & Logistics
10K-50K
Founded 2012
View All Jobs 50

Description

  • Formulate complex driving tasks as reinforcement learning problems with well-shaped rewards and expressive state/action representations.
  • Design and train model-based deep reinforcement learning agents using GPU-accelerated simulation at massive scale.
  • Improve and maintain the simulator used for reinforcement learning training.
  • Build and maintain distributed training infrastructure in JAX across large compute clusters.
  • Develop agentic optimization systems that automate code improvement, experimentation, metric analysis, and policy iteration.
  • Own the full lifecycle of learned policies from problem definition and reward design to large-scale distributed training and deployment on the vehicle.
  • Help define how learned components integrate with the broader autonomy stack to produce robust, shippable behavior.

Requirements

  • BS, MS, or PhD in CS, EE, Robotics, or a related field.
  • Strong foundation in reinforcement learning and deep learning.
  • 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.
  • Deep understanding of core RL concepts, including policy gradients, value functions, exploration-exploitation, model-based RL, reward shaping, and sim-to-real transfer.
  • Data-driven mindset with experience building experiment pipelines and analyzing training runs.
  • Preferred: publications at top venues such as NeurIPS, ICML, ICLR, CoRL, RSS, or ICRA on RL or learned planning.
  • Preferred: experience building or working with GPU-accelerated simulators for RL training.
  • Preferred: track record of shipping a learned component in a production robotics or autonomous vehicle stack.

Benefits

  • Base salary range of $168,000 to $247,000 USD.
  • Opportunities for equity grants.
  • 401(k) plan with employer matching.
  • 16 weeks of paid parental leave.
  • Medical, dental, and vision benefits.
  • 11 paid holidays.
  • Disability and basic life insurance.
  • Flexible paid time off/vacation for salaried roles, plus 80 hours of paid sick time per year.
  • Wellness benefits, commuter benefits match, and a mental health program.

Interested in this position?

Apply directly on the company website

Apply Now

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