The Principal Machine Learning Engineer will lead the Causal ML pod at DoorDash, focusing on building the next generation of causal decisioning systems for various new verticals. This senior technical leadership role requires extensive experience in causal inference and the ability to translate complex business questions into actionable measurement frameworks. The engineer will establish a company-level causal value metric and ensure its integration into decision systems for product prioritization and budget allocation. The role emphasizes collaboration across teams and influencing senior leaders with clear technical judgment.

• Extensive experience (10+ years) in causal inference, econometrics, experimentation, or causal machine learning.
• Experience leading the design and productionization of causal models and measurement platforms.
• Deep judgment about randomized experiments and observational methods.
• Fluency with advanced causal inference methods such as doubly robust estimation and contextual bandits.
• Strong ML engineering skills with the ability to shape data contracts and modeling pipelines.
• A track record of influencing executives and senior cross-functional partners.

• Lead the Causal ML pod across technical strategy, architecture, execution, and quality.
• Define the company-level causal value metric and its measurement framework.
• Build the causal value metric into a decision system for product prioritization and budget allocation.
• Establish how various experimental and observational methods work together.
• Architect reusable causal capabilities for treatment effect estimation and policy evaluation.
• Set standards for validation, monitoring, and governance of causal estimates.
• Influence senior leaders by translating complex causal evidence into clear decisions.
• Develop senior engineers and scientists through technical direction and coaching.