The Machine Learning Engineer will join a team dedicated to enhancing the DoorDash platform through intelligent systems that improve customer satisfaction and retention. This role focuses on developing advanced machine learning systems that optimize credits and refund decisions, balancing cost efficiency with user experience. The engineer will collaborate with various teams to deploy causal models and optimization algorithms that impact millions of users weekly. This position is critical for driving fairness and efficiency in customer interactions.

• 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 in the full software development lifecycle, including designing, generating code, testing, monitoring, and releasing software.
• Deep expertise in statistical modeling and causal inference.
• Experience designing and deploying optimization algorithms.
• 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.
• Excellent communication skills and a track record of cross-functional leadership.

• Design and deploy causal inference models to assess the impact of refunds and credits on customer satisfaction.
• Develop optimization frameworks that balance customer experience with operational cost.
• Build personalized decision systems that adapt to customer preferences in real time.
• Collaborate with engineering, product, and data science partners to shape the roadmap for trust and service recovery.
• Lead end-to-end model development, including experimentation, deployment, monitoring, and iteration.