Member of Technical Staff, Applied AI Research

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.

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.

• 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 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. 

• 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.

• 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

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.

• 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.  

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