Staff Machine Learning Engineer - DashPass

About this role

About the Team

DashPass is DoorDash’s subscription loyalty program that delivers lower delivery fees and a host of additional benefits and value to a large subscriber base of both paid and sponsored subscriptions. DashPass subscribers enjoy lower delivery fees, faster ETAs, 3rd party partnerships, and special discounts and promotions to get maximum value of their membership.

Several teams are part of the DashPass org including Growth, Habituation, Member Experience, Exclusive Offers, and Partnerships. All of these teams require personalized targeting of offers and promotions to entice active Doordash consumers to sign up for DashPass and actively engage with their subscription in a personalized way, as well as reduce churn by offering personalized incentives to DashPass subscribers to keep using their subscription.

We are forming a new team that will leverage AI and advanced ML to power decision making in real-time – from personalized sign up promotions to progressive reward systems, and to pre-cancel offers that retain subscribers.

DashPass is continuously building new benefits and offerings to drive more value for our subscribers, and personalization is our next big bet to help efficiently grow our subscriber base to 2030 and beyond.

About the Role

We’re looking for a Staff Machine Learning Engineer to drive the design and development of large-scale ML/optimization systems to target personalization efforts across the DashPass Subscriber journey.

You're excited about this opportunity because...

• Contribute to Causal inference modeling to measure the incremental impact of DashPass Subscriber acquisition and retention strategies.

• Incentive optimization frameworks that personalize progressive rewards to improve spend efficiency.

• Budget allocation and forecasting models that identify optimal spend across acquisition, referrals, and retention.

• Partner closely with Product, Data Science, and Engineering teams to design experiments, model frameworks, and production ML systems that directly impact DashPass subscriber growth metrics.

• Provide technical mentorship and guidance to engineers and cross-functional partners — leading through influence, not management.

• Build and deploy 0→1 ML systems that improve subscriber outcomes and marketplace health.

• Set best practices for model training, evaluation, deployment, and monitoring

This is a highly impactful IC role for someone who enjoys combining economic intuition, large-scale ML modeling, and system design to solve complex real-world optimization problems.

We’re excited about you because you have…

• M.S. or Ph.D. in Computer Science, Machine Learning, Statistics, or a related field.

• 8+ years of industry experience building production-scale ML systems.

• 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

• Strong understanding of probability theory, statistics, and machine learning fundamentals.

• Strong programming skills in Python, Java, or C++, and experience with ML frameworks such as TensorFlow, PyTorch, or XGBoost.

• Interest in building and leading a new team that has broad impact across a wide range of problem spaces to support a critical business line.

• Proven ability to lead cross-functional initiatives and drive complex technical projects end-to-end.

• Excellent communication skills — able to explain technical concepts to product, business, and engineering audiences.

• Experience in subscriptions growth or marketplace systems is a plus.

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.

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