Causal ML Engineer, Production Systems for Growth

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

Benefits offered by this job

401(k) match

Parental leave (16 weeks)

Wellness benefits

Commuter benefits match

Paid time off

Paid sick leave

Medical benefits

Dental benefits

Vision benefits

11 paid holidays

Disability insurance

Life insurance

Family-forming assistance

Mental health program

Job summary

DoorDash seeks a Causal Machine Learning Engineer to build the causal ML foundation behind how DoorDash grows New Verticals. We are looking for someone with production experience in causal systems such as uplift models, heterogeneous treatment effects, surrogate metrics, experimentation platforms, and off-policy evaluation.

You will join a senior pod of causal ML and econometrics experts across ML, Analytics, Product, and Engineering to shape the causal spine for a large-scale consumer

Qualifications

• Deep practical experience with causal inference, econometrics, experimentation, or causal ML.
• Experience shipping models or decision systems in production in consumer marketplaces or high-scale settings.
• Strong judgment on tradeoffs between randomized experiments, observational estimation, and model-based decisioning.

Responsibilities

• Design, build, and productionize causal ML systems influencing marketplace decisions across New Verticals.
• Build uplift and heterogeneous treatment effect models for lifecycle value, promotions, retention, and reactivation.
• Develop counterfactual evaluation frameworks for ranking, recommendations, search, promotions, and interventions.
• Connect experimentation, observational data, and ML decisioning to enable faster, more robust decision-making.

Skills

Causal Inference

Production ML

Experimentation

Econometrics

Python/Scala

Job description

DoorDash seeks a Causal Machine Learning Engineer to build the causal ML foundation behind how DoorDash grows New Verticals. We are looking for someone with production experience in causal systems such as uplift models, heterogeneous treatment effects, surrogate metrics, experimentation platforms, and off-policy evaluation.

You will join a senior pod of causal ML and econometrics experts across ML, Analytics, Product, and Engineering to shape the causal spine for a large-scale consumer