Staff Machine Learning Engineer, Causal Inference
DoorDash USA · San Francisco, CA; Sunnyvale, CA; Los Angeles, CA; Seattle, WA; New York City, NY
About this role
**Staff Machine Learning Engineer — Causal Inference (DoorDash)** ## About the Team DoorDash is building the next generation of causal decisioning systems for New Verticals (grocery, convenience, retail, alcohol, pets, flowers, and other emerging categories). These businesses operate in high-dimensional, messy marketplaces where every consumer, merchant, item, promotion, substitution, search result, and delivery promise creates a causal question. ## About the Role We’re hiring a **Causal Machine Learning Engineer** to help build the causal ML foundation behind how DoorDash grows New Verticals. You’ll work on production causal systems such as **uplift models, heterogeneous treatment effect models, surrogate metrics, experimentation platforms, counterfactual policy evaluation, promotion optimization,** and **marketplace decisioning**. You’ll join a small, senior pod of causal ML and econometrics experts working across **ML, Analytics, Product, and Engineering** to build the causal “spine” for a large-scale consumer marketplace. ## You’ll be excited because you will… - **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. - 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** to move faster while preserving long-term marketplace health. - Partner with econometrics and analytics leaders to choose methods such as **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 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. ## We’re excited about you because you have… - 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 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/evaluate rigorously, and partner with platform teams to deploy. - 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. ## Compensation & Benefits (Summary) - **National base pay range (US, including IL and CO): $203,500 — $299,300 USD** - Base salary is localized by work locat
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