Senior Causal ML Engineer - Production Decisioning

DoorDash; Seattle (WA); On-site; USD 204,000 - 299,000; Full time

Job summary

DoorDash is hiring a Causal Machine Learning Engineer to build production causal systems for New Verticals, including uplift models, experimentation platforms, and counterfactual policy evaluation. You will join a senior pod of causal ML and econometrics experts shaping the causal spine for a large-scale consumer marketplace.

You will design and deploy reliable data pipelines, work with ML, analytics, product, and engineering teams, and translate causal models into decisions for ranking,

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: build reliable pipelines, train models, evaluate them rigorously, and partner with platform teams to put them into production.
• Strong product judgment: connect methods to business decisions, not just offline metrics.
• 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 inference

Econometrics

Production ML

Experimentation

High-scale systems

Cross-functional collaboration

Job description

DoorDash is hiring a Causal Machine Learning Engineer to build production causal systems for New Verticals, including uplift models, experimentation platforms, and counterfactual policy evaluation. You will join a senior pod of causal ML and econometrics experts shaping the causal spine for a large-scale consumer marketplace.

You will design and deploy reliable data pipelines, work with ML, analytics, product, and engineering teams, and translate causal models into decisions for ranking,