The Causal Machine Learning Engineer at DoorDash will be part of a senior team focused on building causal decisioning systems for New Verticals such as grocery and retail. This role involves developing and productionizing causal ML systems that influence marketplace decisions. The engineer will work on uplift models, counterfactual evaluation frameworks, and connect experimentation with observational data. The position requires collaboration with econometrics and analytics leaders to enhance causal reasoning across teams.

• Deep practical experience with causal inference, econometrics, experimentation, or causal ML.
• Experience shipping models or decision systems in production, ideally in consumer marketplaces or related high-scale settings.
• Strong judgment around the tradeoffs between randomized experiments, observational estimation, and model-based decisioning.
• Comfort debating and applying methods such as doubly robust estimation, double ML, IV, and uplift modeling.
• Strong ML engineering ability to build reliable pipelines and evaluate models rigorously.
• Strong product judgment to connect methods to business decisions.

• Design, build, and productionize causal ML systems that influence real marketplace decisions.
• Build uplift and heterogeneous treatment effect models for consumer lifecycle value and promotions.
• Develop counterfactual evaluation frameworks for various marketplace interventions.
• Build systems that connect experimentation, observational data, and ML decisioning.
• Design surrogate metrics and early indicators to help teams move faster.
• Translate causal models into production systems that shape decisions in various areas.
• Raise the bar for causal reasoning across ML teams.