Software Engineer, Machine Learning - Credit & Refund Optimization

About the role

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. We're seeking a 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... Designing and deploying causal inference models to accurately assess the impact of refunds and credits on customer satisfaction, retention, and behavior Developing optimization frameworks that balance customer experience with operational cost, under policy and budget constraints Building personalized decision systems that adapt to customer preferences and platform dynamics in real time Collaborating with engineering, product, and data science partners to shape the roadmap for trust, service recovery, and consumer experience Leading end-to-end model development, including experimentation, deployment, monitoring, and iteration We're excited about you because you have: 3+ years of industry experience delivering machine learning systems with clear business impact, especially in personalization, optimization, or causal inference 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 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 M.S. or Ph.D. in a quantitative field (e.g., Computer Science, Statistics, Operations Research, Economics, Mathematics) Excellent communication skills and a track record of cross-functional leadership 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 applications. We began using Covey Scout for Inbound from August 21, 2023, through December 21, 2023, and resumed using Covey Scout for Inbound again on June 29, 2024. The Covey tool has been reviewed by an independent auditor. Results of the audit may be viewed here: Covey

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