Senior/Staff Deep Reinforcement Learning Engineer

About The Team

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.

About The Role

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.

You’re excited about this opportunity because you will…

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.

We’re excited about you because…

BS/MS/PhD in CS, EE, Robotics, or a related field, with a strong foundation in reinforcement learning and deep learning.

You have proficiency in using AI coding tools (e.g., Claude Code, Codex, Cursor) in the full software development lifecycle, including designing, generating code, testing, monitoring and releasing software

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.

Nice To Have

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.

About The Team

The Storage teams build and operate online stateful systems and abstractions that are reliable, efficient, secure and easy to use for DoorDash Engineering. The teams are responsible for understanding Product Engineering’s evolving needs and developing platform and infrastructure capabilities to serve them. The team currently supports CockroachDB, Cassandra, Kafka and Redis as well as data abstraction services to reduce the complexity of interacting with storage systems for Product Engineers.

About The Role

We’re hiring a

Data Solutions Engineer

with deep expertise in distributed databases, particularly Apache Cassandra, Redis, Kafka, and database agnostic abstractions. In this role, you will design, optimize, and scale distributed data access layers that power DoorDash’s most critical systems, ensuring high availability, low latency, and fault tolerance.

You’ll serve as a hands-on architect and technical partner to product engineering and infrastructure teams, helping translate complex business requirements into resilient and scalable data models. Your work will directly influence the evolution of

Taulu

, DoorDash’s unified storage abstraction layer, by shaping best practices and identifying platform gaps through real world engagements.

This is a

high-impact, cross functional role

that combines deep technical expertise with a customer centric approach. You’ll lead solutioning engagements from design through production, drive the adoption of Taulu modeling best practices, and ensure that our systems meet goals around reliability, cost efficiency, and velocity. You must be located in San Francisco, Sunnyvale, Seattle or New York for this hybrid opportunity.

You’re excited about this opportunity because you will…

Design and implement

highly scalable, fault tolerant distributed database solutions using Taulu, Apache Cassandra, Redis, Kafka, and other paved path storage solutions.

Architect and optimize

multi-region, globally distributed systems to meet our high standards for availability, latency, and throughput.

Lead data modeling, performance tuning, and capacity planning

for large-scale, mission-critical storage workloads.

Partner with product engineering and infrastructure teams

to deeply understand domain specific data needs and guide them in adopting paved path storage solutions.

Serve as the DRI for solutioning engagements

, owning modeling in Taulu from experimentation through launch and scale.

Shape the evolution of Taulu

by identifying abstraction gaps and converting customer feedback into platform improvements.

Apply workload-aware design

patterns, including caching strategies, partitioning, and consistency tuning to improve performance and efficiency.

Drive adoption of operational best practices

across observability, schema design, capacity planning, and cost optimization across storage systems.

Promote clarity and continuity

by contributing to solutioning playbooks, decision logs, and architectural documentation.

We’re excited about you because…

You have 10+ years of experience designing and scaling distributed data systems, with deep expertise in NoSQL technologies like Apache Cassandra, DynamoDB, or ScyllaDB.

You have a strong command of distributed system concepts such as replication, partitioning, tunable consistency, and failure recovery.

You’ve led data modeling efforts for high-throughput, low-latency workloads and understand the real-world trade-offs involved in NoSQL schema design.

You are experienced with caching technologies like Redis or Memcached and know how to layer them effectively over storage systems to optimize for performance and cost.

You have a customer-first mindset, and thrive when working closely with product and platform teams to translate complex requirements into clean, scalable data models.

You are skilled at communicating complex architecture decisions and building alignment across infrastructure and product engineering organizations.

You have a track record of mentoring engineers, influencing data architecture at scale, and fostering best practices in reliability, observability, and data access patterns.

You document decisions, share learnings, and take pride in contributing to reusable playbooks and durable frameworks for others to build upon.

Bonus: You’ve worked on or contributed to open-source distributed databases.

Notice Regarding Use Of Ai And Automated Tools

To streamline our hiring process, DoorDash utilizes an automated recruitment tool called Gem.

How It Works

Gem assists our recruiting team by evaluating job related qualifications and characteristics in connection with hiring. The tool is designed and used to support - rather than replace - human decision-making; trained personnel make final decisions with meaningful human review and oversight, and DoorDash does not use Gem or other AI-enabled tool  in a manner that has the effect of subjecting applicants or employees to discrimination based on any protected characteristic or proxy or for engaging in any protected activity under applicable law.

Data Retention, Privacy & Bias Audit:

Data collected during this process is retained in accordance with our

Candidate Privacy Policy

and applicable state laws. In compliance with New York City Local Law 144, the independent bias audit summary for Gem is publicly available for review at our

Careers Page

.

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

Compensation

The successful candidate’s starting pay will fall within the pay range listed below and is determined based on job-related factors including, but not limited to, skills, experience, qualifications, work location, and market conditions. Base salary is localized according to an employee’s work location. Ranges are market-dependent and may be modified in the future.

In addition to base salary, the compensation for this role includes opportunities for equity grants. Talk to your recruiter for more information.

DoorDash cares about you and your overall well-being. That’s why we offer a comprehensive benefits package to all regular employees, which includes a 401(k) plan with employer matching, 16 weeks of paid parental leave, wellness benefits, commuter benefits match, paid time off and paid sick leave in compliance with applicable laws (e.g. Colorado Healthy Families and Workplaces Act). DoorDash also offers medical, dental, and vision benefits, 11 paid holidays, disability and basic life insurance, family-forming assistance, and a mental health program, among others.

To learn more about our benefits, visit our