Causal ML Engineer: Production Uplift & Policy

Visa Hunt; San Francisco, Sunnyvale (CA, 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 and paid sick leave

Job summary

DoorDash is hiring a Causal Machine Learning Engineer to build the causal ML spine behind how we grow New Verticals. You will join a senior pod of causal ML and econometrics experts to develop uplift models, counterfactual evaluation, and production-ready causal systems that influence ranking, recommendations, and marketplace interventions.

You will connect experimentation, observational data, and ML decisioning, designing surrogate metrics and ensuring robust causal reasoning across teams.

Qualifications

• Deep practical experience with causal inference, econometrics, experimentation, or causal ML.
• Experience shipping models or decision systems in production, ideally in consumer marketplaces, ads, recommendations, search, pricing, promotions, logistics, fintech, or other high-scale settings.
• Strong judgment around the tradeoffs between randomized experiments, observational estimation, and model-based decisioning.
• Comfort debating and applying methods such as doubly robust estimation, double ML, IV, diff-in-diff, CUPED, uplift modeling, contextual bandits, and off-policy evaluation
• Strong ML engineering ability: you can build reliable pipelines, train models, evaluate them rigorously, and partner with platform teams to put them into production.
• Strong product judgment: you can connect methods to business decisions, not just optimize offline metrics.
• The ability to operate across functions with ML engineers, economists, data scientists, product managers, and business leaders.

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.
• Raise the bar for causal reasoning across ML teams: when to trust a model, when not to, and how to debug causal claims in a real marketplace.

Skills

Causal ML

Econometrics

Experimentation

Uplift models

TE models

Counterfactual evaluation

Production systems

ML engineering

Product judgment

Cross-functional collaboration

Job description

DoorDash is hiring a Causal Machine Learning Engineer to build the causal ML spine behind how we grow New Verticals. You will join a senior pod of causal ML and econometrics experts to develop uplift models, counterfactual evaluation, and production-ready causal systems that influence ranking, recommendations, and marketplace interventions.

You will connect experimentation, observational data, and ML decisioning, designing surrogate metrics and ensuring robust causal reasoning across teams.