Senior/Staff Deep Reinforcement Learning Engineer

DoorDash US
San Francisco
Workplace: OnsiteFull timeFunction: Education & TrainingEducation: bachelorsSkills: ["Problem-solving","Data-driven","Communication"]

Design, train, and deploy deep reinforcement learning policies for real-time driving decisions in autonomous vehicles. Own the full lifecycle from problem formulation to large-scale distributed training and on-vehicle inference, leveraging a pure JAX stack and GPU-accelerated simulation to produce robust, ship-ready autonomy components.

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FursaFursa
DoorDash US
DoorDash US
4 months ago

Senior/Staff Deep Reinforcement Learning Engineer

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI
Last checked: 1 hour agoStatus: Live

Job Summary

Design, train, and deploy deep reinforcement learning policies for real-time driving decisions in autonomous vehicles. Own the full lifecycle from problem formulation to large-scale distributed training and on-vehicle inference, leveraging a pure JAX stack and GPU-accelerated simulation to produce robust, ship-ready autonomy components.
Location: San Francisco
Workplace: Onsite
Employment Type: Full time
Job Function: Education & Training
Seniority: Sr. Director level

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, and iterate on RL policies with minimal human intervention.
  • •Own the full lifecycle from problem formulation through large-scale distributed training to on-vehicle inference.

Pay and Benefits

Perks:Health Insurance401kWellness StipendPaid Leave

Key Requirements

  • •BS/MS/PhD in CS, EE, Robotics, or a related field, with a 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 grasp of core RL concepts: policy gradients, value functions, exploration-exploitation, model-based RL, reward shaping, and sim-to-real transfer.
  • •Data-driven mindset: comfortable building experiment pipelines, analyzing training runs, and letting metrics guide architectural decisions.
Experience:Autonomous vehiclesRoboticsReinforcement learning
Education:Bachelor's
Skills:Problem-solvingData-drivenCommunication
Languages:English
Tech Stack:JAXGPU-accelerated simulationJITVectorized environmentsReinforcement learningPolicy gradients

Company Brief

DoorDash US
On-demand logistics platform connecting consumers with local restaurants, grocers, and retailers for food delivery, pickup, and convenience services. Operates a marketplace and last-mile delivery network while offering tools and analytics for merchants and couriers.
Industry: Online Marketplaces
Company Size: Enterprise (1,001+ employees)
Revenue: USD 1B+
Growth: Public Company
Valuation: Public Company (Market Cap in USD)
Funding: IPO / Publicly Listed
Headquarters: San Francisco, United States
Founded: 2013
WebsiteLinkedIn