Causal ML Engineer - Production Systems for Marketplaces

DoorDash; New York (NY); On-site; USD 204,000 - 299,000; Full time

Benefits offered by this job

401(k) plan with employer matching

Paid parental leave

Wellness benefits

Commuter benefits match

Paid time off

Job summary

DoorDash is hiring a Causal Machine Learning Engineer to build the causal ML foundation behind how DoorDash grows New Verticals. You will join a senior pod of causal ML and econometrics experts, crafting systems that connect experimentation, observational data, and ML decisioning to influence ranking, pricing, and growth.

We expect deep experience in causal inference and production ML, plus strong cross-functional collaboration with economists, data scientists, and product leaders.

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, or other high-scale settings.
• Strong judgment around 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: build reliable pipelines, train models, evaluate them rigorously, and partner with platform teams to productionize.
• Strong product judgment: connect methods to business decisions, not just offline metrics.
• Ability to operate across ML engineers, economists, data scientists, product managers, and business leaders.

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 marketplace interventions.
• Create systems that connect experimentation, observational data, and ML decisioning to inform tradeoffs when experiments are slow or noisy.
• Design surrogate metrics and early indicators to accelerate progress while protecting long-term health.
• Collaborate with econometrics and analytics leaders to choose methods like doubly robust estimation, IV, diff-in-diff, synthetic controls, and off-policy evaluation.

Skills

Causal inference

Experimentation

Econometrics

Production ML

ML engineering

Product judgment

Cross-functional collaboration

Job description

DoorDash is hiring a Causal Machine Learning Engineer to build the causal ML foundation behind how DoorDash grows New Verticals. You will join a senior pod of causal ML and econometrics experts, crafting systems that connect experimentation, observational data, and ML decisioning to influence ranking, pricing, and growth.

We expect deep experience in causal inference and production ML, plus strong cross-functional collaboration with economists, data scientists, and product leaders.