Member of Technical Staff, Applied AI Research
DoorDash USA
| Company | DoorDash USA |
| Category | Data & Analytics |
| Location | San Francisco |
| Remote | On-site (inferred) |
| Employment | Not stated |
| Level | Not stated |
| Salary | Not stated by the employer |
| Posted | 13 Mar 2026 |
| Last verified | 9 Aug 2026 |
| Source | Employer ATS (greenhouse) |
Description
DoorDash’s mission is to empower local economies. With AI, we believe there’s a huge opportunity to leverage agentic technology to help businesses grow their sales, to create more personalized experiences for our customers, to make our logistics system even more efficient.
We have an applied AI research lab at DoorDash, which is a team that is small, highly motivated, and focused on engineering excellence. This team is for individuals who appreciate challenging themselves and thrive on curiosity. We operate with a flat structure - everyone is expected to be hands-on and contribute directly to the mission. Initiative and consistent delivery are how people grow and lead here.
About the Role
You'll work directly with our cofounder with direct ownership over the direction of AI research at DoorDash. In this role you will develop realistic agent environments and evaluation methods, design systems and algorithms to improve agent performance, and collect and curate human and synthetic data to improve the capability and behavior of agent systems. This is hands-on work with immediate product impact at real-world scale.
You're excited about this opportunity because you will…
Work directly with leadership and see your research deployed at real-world scale - small team, massive reach.
Move fast without bureaucracy - rapid iteration, high autonomy, and no layers of politics.
Have access to global data scale, robust compute, and a platform primed for AI transformation.
Shape how AI transforms commerce, building intelligent and adaptive experiences for millions of Consumers, Merchants, and Dashers.
We're excited about you because you have…
Experience with the latest AI tools building AI products
Familiarity with building agent solutions, including context management, defining evals, measuring performance
Strong knowledge of reinforcement learning techniques
Experience building infra for large-scale reinforcement learning and multi-agent reinforcement learning
Experience at frontier labs (OpenAI, Anthropic, DeepMind) or top AI startups.
A builder mindset - you're energized by experimentation, move quickly, and stay close to the work
Strong cross-functional instincts and a natural ability to bring research and product teams together.
Nice to have: PhD with AI or other scientific domains
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 v