Senior/Staff Deep Reinforcement Learning Engineer

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.

We’re excited about you because...

• You have a strong foundation in reinforcement learning and deep learning.
• You have proficiency in using AI coding tools in the full software development lifecycle, including designing, generating code, testing, monitoring, and releasing software.
• You have hands-on experience training RL agents at scale, ideally in robotics, autonomous driving, or other real-time decision-making domains.
• You are proficient in JAX or a similar functional ML framework; you are comfortable with JIT compilation, vectorized environments, and GPU-accelerated simulation.
• You have a deep grasp of core RL concepts: policy gradients, value functions, exploration-exploitation, model-based RL, reward shaping, and sim-to-real transfer.
• You have a data-driven mindset: you are 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.

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

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.