Software Engineer, Machine Learning - Credit & Refund Optimization

Job description

About the Team

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 Role

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
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