D

DoorDash

Runs apps for local food and grocery delivery

Senior/Staff Deep Reinforcement Learning Engineer

Robotics & Autonomous SystemsConfirmed live in the last 24 hours
Full-timeSan Francisco, CA, USAIn-person

About the Job

Requirements

  • A Bachelor’s, Master’s, or PhD degree in Computer Science, Electrical Engineering, Robotics, or a related field, with a strong foundation in reinforcement learning and deep learning.
  • Proficiency in using AI coding tools such as Claude Code, Codex, or Cursor throughout the software development lifecycle, including designing, generating code, testing, monitoring, and releasing software.
  • Hands-on experience training reinforcement learning agents at scale, ideally in robotics, autonomous driving, or other real-time decision-making domains.
  • Proficiency in JAX or a similar functional machine learning framework, with comfort using just-in-time compilation, vectorized environments, and GPU-accelerated simulation.
  • Deep understanding of policy gradients, value functions, exploration-exploitation, model-based reinforcement learning, reward shaping, and sim-to-real transfer.
  • Experience building experiment pipelines, analyzing training runs, and using metrics to guide architectural decisions.

Responsibilities

  • Formulate complex driving tasks as reinforcement learning problems with well-shaped reward functions and expressive state/action representations.
  • Design and train model-based deep reinforcement learning agents using GPU-accelerated simulation at massive scale, including improving the simulator.
  • 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 reinforcement learning policies with minimal human intervention.
  • Design, train, and deploy deep reinforcement learning policies that make real-time driving decisions for autonomous vehicles.
  • Own the full lifecycle, from problem formulation and reward design through large-scale distributed training to on-vehicle inference.
  • Help define how learned components compose with the rest of the autonomy stack to produce robust, shippable behavior.

Desired Qualifications

  • Publications at top venues such as NeurIPS, ICML, ICLR, CoRL, RSS, or ICRA on reinforcement learning or learned planning.
  • Experience building or working with GPU-accelerated simulators for reinforcement learning training.
  • Track record of shipping a learned component in a production robotics or autonomous vehicle stack.

About the Company

DoorDash

Runs apps for local food and grocery delivery

DoorDash runs apps that let people order restaurant meals, groceries, and other goods from local businesses, which independent couriers called Dashers bring to their doors. In 2019 it overtook Grubhub to become the largest food delivery service in the United States, and it later expanded abroad by buying Finland's Wolt and Britain's Deliveroo. Merchants pay DoorDash a fee on each order and can buy ads in its apps, while customers pay delivery and service fees or join DashPass, a subscription that cuts those fees. Businesses can also hire Dashers through its Drive service to carry orders placed on their own websites. Four Stanford students founded DoorDash in 2013, and it is based in San Francisco, California.

Company size
10,001+
Company stage
IPO
Headquarters
San Francisco, CA
Founded
2013

Recently Posted Jobs

Explore more opportunities at DoorDash

See more jobs