Causal ML Engineer, Production Marketplace Systems

DoorDash, Inc.; San Francisco (CA); Presencial; USD 204.000 - 299.000; Jornada completa

Descripción de la vacante

DoorDash is building causal ML foundations for New Verticals, crafting uplift and evaluation systems that influence real marketplace decisions across consumer segments. We seek a senior Causal Machine Learning Engineer to design production causal models and frameworks for ranking, promotions, and optimization.

The role requires deep expertise in causal inference, production ML, and the ability to collaborate with economists, data scientists, and engineers to ship scalable solutions that impact

Formación

• Deep practical experience with causal inference, econometrics, experimentation, or causal ML.
• Experience shipping models or decision systems in production, ideally in consumer marketplaces, ads, recommendations, search, pricing, promotions, logistics, or other high-scale settings.
• Strong judgment around the tradeoffs between randomized experiments, observational estimation, and model-based decisioning.

Responsabilidades

• Design, build, and productionize causal ML systems that influence real marketplace decisions across New Verticals.
• Build uplift / heterogeneous treatment effect models for consumer lifecycle value, promotions, retention, and reactivation.
• Develop counterfactual evaluation frameworks for ranking, recommendations, search, promotions, substitutions, and marketplace interventions.
• Build systems that connect experimentation, observational data, and ML decisioning to improve tradeoffs when randomized experiments are slow, noisy, or incomplete.
• Design surrogate metrics and early indicators to move faster while preserving long-term marketplace health.
• Partner with econometrics and analytics leaders to choose the right methods such as doubly robust estimation, IV, diff-in-diff, synthetic controls, double ML, CUPED-style variance reduction, contextual bandits, and off-policy evaluation.
• Translate causal models into production systems that shape decisions in ranking, targeting, budget allocation, inventory-aware discovery, and consumer growth.
• Raise the bar for causal reasoning across ML teams: when to trust a model, when not to, and how to debug causal claims in a real marketplace.

Conocimientos

causal inference

Descripción del empleo

DoorDash is building causal ML foundations for New Verticals, crafting uplift and evaluation systems that influence real marketplace decisions across consumer segments. We seek a senior Causal Machine Learning Engineer to design production causal models and frameworks for ranking, promotions, and optimization.

The role requires deep expertise in causal inference, production ML, and the ability to collaborate with economists, data scientists, and engineers to ship scalable solutions that impact