Causal ML Engineer for Marketplace Growth

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

Job summary

DoorDash is hiring a Causal Machine Learning Engineer to build the causal ML foundation behind how DoorDash grows New Verticals. You will design, build, and productionize causal ML systems that influence marketplace decisions, including uplift models for lifecycle value, promotions, and retention.

You will join a senior pod of causal ML and econometrics experts, translating models into production decisions that impact ranking, targeting, and inventory-aware discovery at scale.

Qualifications

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

Responsibilities

• Design, build, and productionize causal ML systems that influence marketplace decisions across New Verticals.
• Build uplift/heterogeneous treatment effect models for customer Lifecycle value, promotions, retention, and reactivation.
• Develop counterfactual evaluation frameworks for ranking, recommendations, search, promotions, substitutions, and interventions.
• Translate causal models into production systems that shape decisions in ranking, targeting, and budget allocation.

Skills

Causal inference

Experimentation

Production ML

Econometrics

ML engineering

Job description

DoorDash is hiring a Causal Machine Learning Engineer to build the causal ML foundation behind how DoorDash grows New Verticals. You will design, build, and productionize causal ML systems that influence marketplace decisions, including uplift models for lifecycle value, promotions, and retention.

You will join a senior pod of causal ML and econometrics experts, translating models into production decisions that impact ranking, targeting, and inventory-aware discovery at scale.