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.  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. Notice Regarding Use of AI and Automated Tools:  To streamline our hiring process, DoorDash utilizes an automated recruitment tool called Gem. How it works: Gem assists our recruiting team by evaluating job related qualifications and characteristics in connection with hiring. The tool is designed and used to support - rather than replace - human decision-making; trained personnel make final decisions with meaningful human review and oversight, and DoorDash does not use Gem or other AI-enabled tool  in a manner that has the effect of subjecting applicants or employees to discrimination based on any protected characteristic or proxy or for engaging in any protected activity under applicable law. Data Retention, Privacy & Bias Audit: Data collected during this process is retained in accordance with our Candidate Privacy Policy and applicable state laws. In compliance with New York City Local Law 144, the independent bias audit summary for Gem is publicly available for review at our Careers Page .  Notice to Applicants for Jobs Located in NYC or Remote Jobs Associated With Office in NYC Only We use Covey as part of our hiring and/or promotional process for jobs in NYC and certain features may qualify it as an AEDT in NYC. As part of the hiring and/or promotion process, we provide Covey with job requirements and candidate submitted applicatio