Senior/Staff Deep Reinforcement Learning Engineer

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. Our models jointly handle prediction and planning in a single unified architecture. Our stack is pure JAX end-to-end: the same code you train with is the code that runs on the robot. No C++ rewrites, no TensorRT export. A new policy goes from training to on-vehicle deployment in minutes.

As a Senior/Staff Deep RL Engineer, you will design, train, and deploy deep reinforcement learning policies that make real-time driving decisions for our autonomous vehicles. You will own the full lifecycle, from problem formulation and reward design through large-scale distributed training to on-vehicle inference. You'll help define how learned components compose with the rest of the autonomy stack to produce robust, shippable behavior.

• 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.

• 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.

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

Notice to Applicants for Jobs Located in NYC or Remote Jobs Associated With Office in NYC Only

We use Covey as part of our hiring and/or promotional process for jobs in NYC and certain features may qualify it as an AEDT in NYC. As part of the hiring and/or promotion process, we provide Covey with job requirements and candidate submitted applications. We began using Covey Scout for Inbound from August 21, 2023, through December 21, 2023, and resumed using Covey Scout for Inbound again on June 29, 2024.

The Covey tool has been reviewed by an independent auditor. Results of the audit may be viewed here: Covey

At DoorDash, our mission to empower local economies shapes how our team members move quickly, learn, and reiterate in order to make impactful decisions that display empathy for our range of users—from Dashers to merchant partners to consumers. We are a technology and logistics company that started by enabling door-to-door delivery, and we are looking for team members who can help us go from a company that is known as the place you order food to a company that people turn to for any and all goods.

DoorDash is growing rapidly and changing constantly, which gives our team members the opportunity to share their unique perspectives, solve new challenges, and own their careers. We're committed to supporting employees’ happiness, healthiness, and overall well-being by providing comprehensive benefits and perks including premium healthcare, wellness expense reimbursement, paid parental leave and more.

We’re committed to growing and empowering a more inclusive community within our company, industry, and cities. That’s why we hire and cultivate diverse teams of people from all backgrounds, experiences, and perspectives. We believe that true innovation happens when everyone has room at the table and the tools, resources, and opportunity to excel.

Statement of Non-Discrimination: In keeping with our beliefs and goals, no employee or applicant will face discrimination or harassment based on: race, color, ancestry, national origin, religion, age, gender, marital/domestic partner status, sexual orientation, gender identity or expression, disability status, or veteran status. Above and beyond discrimination and harassment based on “protected categories,” we also strive to prevent other subtler forms of inappropriate behavior (i.e., stereotyping) from ever gaining a foothold in our office. Whether blatant or hidden, barriers to success have no place at DoorDash. We value a diverse workforce – people who identify as women, non-binary or gender non-conforming, LGBTQIA+, American Indian or Native Alaskan, Black or African American, Hispanic or Latinx, Native Hawaiian or Other Pacific Islander, differently-abled, caretakers and parents, and veterans are strongly encouraged to apply. Thank you to the Level Playing Field Institute for this statement of non-discrimination.

Pursuant to the San Francisco Fair Chance Ordinance, Los Angeles Fair Chance Initiative for Hiring Ordinance, and any other state or local hiring regulations, we will consider for employment any qualified applicant, including those with arrest and conviction records, in a manner consistent with the applicable regulation.