Staff Machine Learning Engineer - DashPass

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.

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.

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

8+ years of industry experience building production-scale ML systems. , 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. Notice Regarding Use of AI and Automated Tools: To streamline our hiring process, DoorDash utilizes an automated recruitment tool called Gem.

Qualifications

and characteristics in connection with hiring.