Staff Software Engineer, Machine Learning - Personalization

About this role

About the Team

Come help us build the world's most reliable on-demand, logistics engine for last-mile retail delivery! We're looking for an experienced machine learning engineer to help us develop modern growth and personalization models that power DoorDash's growing retail and grocery business.

About the Role

We’re looking for a passionate Applied Machine Learning expert to join our team. As a Staff Machine Learning Engineer, you’ll be conceptualizing, designing, implementing, and validating algorithmic improvements to the growth and personalization experiences at the heart of our fast-growing grocery and retail delivery business. You will use our robust data and machine learning infrastructure to implement new ML solutions to make the consumer search experience more relevant, seamless, and delightful across grocery, convenience, and many other retail categories. You will demonstrate a strong command of production level machine learning, experience with solving end-user problems, and collaborate well with multi-disciplinary teams.

You will report into the engineering manager on our Personalization team. We expect this role to be hybrid with some time in-office and some time remote (#LI-Hybrid).

You’re excited about this opportunity because you will…

• Develop production machine learning solutions to build a world class personalized shopping experience for a diverse and expanding retail space

• Partner with engineering and product leaders to help shape the product roadmap applying ML

• Mentor junior team members, and lead cross functional pods to create collective impact

You can find out more on our ML blog here

We’re excited about you because you have…

• 8+ years of industry experience developing machine learning models with business impact, and shipping ML solutions to production.

• 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

• M.S., or PhD. in Statistics, Computer Science, Math, Operations Research, Physics, Economics, or other quantitative field

• Expertise in applied ML for Causal Inference and Recommendation Systems - both classical and deep learning based. Additional familiarity with explore/exploit/MAB algorithms & LLMs is a plus.

• Machine learning background in Python; experience with PyTorch or TensorFlow preferred.

• Ability to communicate technical details to nontechnical stakeholders

• You keep the mission in mind, take ideas and help them grow using data and rigorous testing, show evidence of progress and then double down

• Desire for impact with a growth-minded and collaborative mindset

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

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