Join the team focused on building intelligent, personalized systems that drive fairness, efficiency, and trust in the DoorDash platform. We own the credits and refunds experience—key components of customer satisfaction and retention—and we’re pioneering new ways to optimize and personalize these decisions at scale using causal inference and optimization.
About the RoleWe're seeking a Staff Machine Learning Engineer to lead the development of state-of-the-art ML systems that personalize and optimize credits and refund decisions. This work is critical to balancing cost efficiency with long-term customer retention and experience.
In this high-impact role, you will partner with cross-functional leaders to design and deploy causal models and optimization algorithms that influence millions of user experiences every week.
You're excited about this opportunity because you will… Design and deploy causal inference models to accurately assess the impact of refunds and credits on customer satisfaction, retention, and behavior Develop optimization frameworks that balance customer experience with operational cost, under policy and budget constraints Build personalized decision systems that adapt to customer preferences and platform dynamics in real time Collaborate with engineering, product, and data science partners to shape the roadmap for trust, service recovery, and consumer experience Lead end-to-end model development, including experimentation, deployment, monitoring, and iteration We're excited about you because you have… M.S. or Ph.D. in a quantitative field (e.g., Computer Science, Statistics, Operations Research, Economics, Mathematics) 6+ years of industry experience delivering machine learning systems with clear business impact, especially in personalization, optimization, or causal inference Deep expertise in statistical modeling and causal inference (e.g., uplift modeling, treatment effect estimation, synthetic controls, instrumental variables) Experience designing and deploying optimization algorithms (e.g., multi-objective optimization, bandits, constrained optimization) Proficiency in Python and ML tooling such as PyTorch, Spark, and MLflow A strong product sense and ability to translate business objectives into technical solutions Excellent communication skills and a track record of cross-functional leadership Hands-on leadership and strong product intuition Humility - willing to get into the details and open to feedback Growth mindset - you're eager to expand your skill set and excited to carve out your career path in a hyper-growth setting Adaptability, resiliency, and ability to thrive in ambiguity - things can change quickly, and you'll need to help the team evolve
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