Senior Causal ML Engineer - Production Systems

DoorDash; Los Angeles (CA); On-site; USD 204,000 - 299,000; Full time

Benefits offered by this job

Equity grants

401(k) matching

Paid parental leave

Wellness benefits

Commuter benefits match

Paid time off

Paid sick leave

Medical, dental and vision benefits

11 paid holidays

Disability and life insurance

Mental health program

Job summary

DoorDash is hiring a Causal Machine Learning Engineer to help build the causal ML foundation behind growing New Verticals. You will join a small, senior pod of causal ML and econometrics experts working across ML, Analytics, Product, and Engineering to shape a scalable causal spine for a large-scale consumer marketplace.

You will design, build, and productionize causal ML systems that influence ranking, targeting, and marketplace interventions, and develop uplift models, counterfactual

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.
• Build systems that connect experimentation, observational data, and ML decisioning so teams can make better tradeoffs when randomized experiments are slow, noisy, or incomplete.
• Design surrogate metrics and early indicators that help teams move faster while preserving long-term marketplace health.
• Partner with econometrics and analytics leaders to choose the right methods: doubly robust estimation, IV, diff-in-diff, synthetic controls, double ML, CUPED-style variance reduction, contextual bandits, off-policy evaluation, and related approaches.
• Translate causal models into production systems that can 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 is hiring a Causal Machine Learning Engineer to help build the causal ML foundation behind growing New Verticals. You will join a small, senior pod of causal ML and econometrics experts working across ML, Analytics, Product, and Engineering to shape a scalable causal spine for a large-scale consumer marketplace.

You will design, build, and productionize causal ML systems that influence ranking, targeting, and marketplace interventions, and develop uplift models, counterfactual