Staff Machine Learning Engineer, Causal Inference
Core
Building the causal ML foundation for DoorDash's New Verticals (grocery, retail, alcohol, etc.) to influence real marketplace decisions.
Role type
Staff Machine Learning Engineer (Causal Inference)
Builds
Production causal ML systems, uplift models, counterfactual evaluation frameworks, and surrogate metrics for a large-scale consumer marketplace.
Domain
Consumer marketplace, causal inference, econometrics
Deliverable
production ML models
Required skills
causal inference, econometrics, experimentation, production ML engineering, product judgment, cross-functional collaboration
Preferred skills
experience in consumer marketplaces, ads, recommendations, search, pricing, promotions, logistics, or fintech
Technologies
doubly robust estimation, IV, diff-in-diff, synthetic controls, double ML, CUPED, contextual bandits, off-policy evaluation
Responsibilities
Design and productionize causal ML systems; build uplift and heterogeneous treatment effect models; develop counterfactual evaluation frameworks; design surrogate metrics; partner with econometrics leaders on method selection; translate causal models into production decisioning systems.
Seniority
Staff, hands-on IC with strategic impact
