Causal ML Engineer for Marketplace Growth

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

Benefits offered by this job

401(k) plan with employer matching

16 weeks paid parental leave

Wellness benefits

Commuter benefits match

Paid time off & paid sick leave

Medical, dental and vision benefits

Job summary

DoorDash is seeking a Causal Machine Learning Engineer to build the causal ML foundation behind how DoorDash grows New Verticals. You will join a small, senior pod of causal ML and econometrics experts delivering scalable causal systems across ML, Analytics, Product, and Engineering.

The role emphasizes production-ready uplift and heterogeneous treatment effect models, counterfactual evaluation, and production pipelines that connect experimentation with ML decisioning to improve ranking,

Qualifications

• Deep practical experience with causal inference, econometrics, experimentation, or causal ML.
• Strong ML engineering ability: build reliable pipelines, train models, evaluate them rigorously.
• Strong product judgment: connect methods to business decisions, not just offline metrics.

Responsibilities

• Design, build, and productionize causal ML systems that influence real marketplace decisions across New Verticals.
• Build uplift / heterogeneous treatment effect models for value, promotions, retention, and reactivation.
• Develop counterfactual evaluation frameworks for ranking, recommendations, search, and marketplace interventions.
• Create systems that connect experimentation, observational data, and ML decisioning to improve tradeoffs.
• Design surrogate metrics and early indicators to move faster while preserving marketplace health.

Job description

DoorDash is seeking a Causal Machine Learning Engineer to build the causal ML foundation behind how DoorDash grows New Verticals. You will join a small, senior pod of causal ML and econometrics experts delivering scalable causal systems across ML, Analytics, Product, and Engineering.

The role emphasizes production-ready uplift and heterogeneous treatment effect models, counterfactual evaluation, and production pipelines that connect experimentation with ML decisioning to improve ranking,