Staff Causal ML Engineer, Production Systems

DoorDash USA; San Francisco (CA); On-site; USD 204,000 - 299,000; Full time

Job summary

DoorDash USA is seeking a Causal Machine Learning Engineer to build the causal ML foundation behind how we grow New Verticals. This role focuses on production causal systems such as uplift models and counterfactual evaluation, interfacing with ML, Analytics, Product, and Engineering teams.

You will join a senior pod of causal ML experts, responsible for creating a scalable causal spine for a large-scale consumer marketplace and translating methods into decisions that impact ranking, promotions,

Qualifications

• Deep practical experience with causal inference, econometrics, experimentation, or causal ML.

Responsibilities

• 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.
• Translate causal models into production systems that shape decisions in ranking, targeting, budget allocation, inventory-aware discovery, and consumer growth.

Skills

Causal inference

Econometrics

Experimentation

Causal ML

Production ML systems

ML engineering

Cross-functional collaboration

Product judgment

Job description

DoorDash USA is seeking a Causal Machine Learning Engineer to build the causal ML foundation behind how we grow New Verticals. This role focuses on production causal systems such as uplift models and counterfactual evaluation, interfacing with ML, Analytics, Product, and Engineering teams.

You will join a senior pod of causal ML experts, responsible for creating a scalable causal spine for a large-scale consumer marketplace and translating methods into decisions that impact ranking, promotions,