Staff Machine Learning Engineer, Causal Inference

DoorDash US
San Francisco, Sunnyvale, Los Angeles, Seattle, New York
Workplace: OnsiteFull timeUSD 203,500 - 299,300 annuallyFunction: Data Science & Machine LearningSkills: ["Product judgment","Cross-functional collaboration","Causal reasoning","Debugging causal claims"]

Build and productionize causal machine learning systems that influence real marketplace decisions for DoorDash’s new verticals. Design uplift and heterogeneous treatment effect models, develop counterfactual evaluation for ranking and recommendations, and connect experimentation and observational data to decisioning when experiments are slow or incomplete. Partner with econometrics and analytics leaders, translate methods into production systems, and raise the bar for causal reasoning across ML teams.

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DoorDash US
DoorDash US
1 day ago

Staff Machine Learning Engineer, Causal Inference

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Last checked: 1 hour agoStatus: Live

Job Summary

Build and productionize causal machine learning systems that influence real marketplace decisions for DoorDash’s new verticals. Design uplift and heterogeneous treatment effect models, develop counterfactual evaluation for ranking and recommendations, and connect experimentation and observational data to decisioning when experiments are slow or incomplete. Partner with econometrics and analytics leaders, translate methods into production systems, and raise the bar for causal reasoning across ML teams.
Location: San Francisco, Sunnyvale, Los Angeles, Seattle, New York
Workplace: Onsite
Employment Type: Full time
Job Function: Data Science & Machine Learning
Seniority: Sr. Manager level

Key Responsibilities

  • •Design, build, and productionize causal ML systems that influence real marketplace decisions across new verticals.
  • •Build uplift/heterogeneous treatment effect models for 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.
  • •Translate causal models into production systems shaping decisions across ranking, targeting, budget allocation, inventory-aware discovery, and consumer growth.

Pay and Benefits

Salary: USD 203,500 - 299,300 annually
Equity and Bonus:Equity
Perks:Health InsuranceDentalVision401kPaid ParentalPaid Leave

Key Requirements

  • •Deep practical experience with causal inference, econometrics, experimentation, or causal ML.
  • •Experience shipping causal models or decision systems in production, ideally in consumer marketplaces or similar high-scale settings.
  • •Strong judgment about 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 to build reliable pipelines, train and evaluate rigorously, and help productionize with platform teams.
Skills:Product judgmentCross-functional collaborationCausal reasoningDebugging causal claims
Tech Stack:Causal inferenceUplift modelingHeterogeneous treatment effectsCounterfactual evaluationSurrogate metricsExperimentation platformsDoubly robust estimationIVDiff-in-diffSynthetic controlsDouble MLCUPEDContextual banditsOff-policy evaluationObservational dataExperimentation data

Company Brief

DoorDash US
On-demand logistics platform connecting consumers with local restaurants, grocers, and retailers for food delivery, pickup, and convenience services. Operates a marketplace and last-mile delivery network while offering tools and analytics for merchants and couriers.
Industry: Online Marketplaces
Company Size: Enterprise (1,001+ employees)
Revenue: USD 1B+
Growth: Public Company
Valuation: Public Company (Market Cap in USD)
Funding: IPO / Publicly Listed
Headquarters: San Francisco, United States
Founded: 2013
WebsiteLinkedIn