Principal Machine Learning Engineer, TEAM

DoorDash US
San Francisco, Sunnyvale, Seattle, New York
Full timeUSD 282,100 - 414,800Function: Data Science & Machine LearningExperience: 10+ yearsSkills: ["Technical leadership","Cross-functional influence","Coaching","Causal reasoning","Evidence-based decisioning"]

Lead the Causal ML pod to establish company-level causal decisioning for DoorDash’s New Verticals marketplace domains. Define and build a durable causal value metric connecting experiments, observational evidence, and production ML so leaders can compare investments reliably. Own the technical strategy and architecture for causal measurement and decision systems across product prioritization, intervention selection, ranking, recommendations, and long-term outcome forecasting, with rigorous validation and governance.

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FursaFursa
DoorDash US
DoorDash US
2 days ago

Principal Machine Learning Engineer, TEAM

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Last checked: 3 hours agoStatus: Live

Job Summary

Lead the Causal ML pod to establish company-level causal decisioning for DoorDash’s New Verticals marketplace domains. Define and build a durable causal value metric connecting experiments, observational evidence, and production ML so leaders can compare investments reliably. Own the technical strategy and architecture for causal measurement and decision systems across product prioritization, intervention selection, ranking, recommendations, and long-term outcome forecasting, with rigorous validation and governance.
Location: San Francisco, Sunnyvale, Seattle, New York
Employment Type: Full time
Job Function: Data Science & Machine Learning
Seniority: Mid level

Key Responsibilities

  • •Lead the Causal ML pod across technical strategy, architecture, execution, and quality, creating a roadmap linking platform foundations to product applications.
  • •Define the company-level causal value metric and measurement framework, including target construct, time horizon, identification strategy, calibration, uncertainty, and guardrails.
  • •Build the metric into a decision system for product prioritization, experiment readouts, intervention selection, budget allocation, and portfolio tradeoffs.
  • •Establish how randomized experiments, quasi-experiments, observational estimation, and learned models work together, making the limits of each source of evidence explicit.
  • •Develop and guide production applications across promotions, lifecycle interventions, ranking, recommendations, search, substitutions, demand shaping, and inventory-aware discovery.

Pay and Benefits

Salary: USD 282,100 - 414,800
Equity and Bonus:Equity
Perks:Health InsuranceDentalVision401kParental LeavePaid LeaveLife InsuranceWellness Stipend

Key Requirements

  • •10+ years of experience in causal inference, econometrics, experimentation, or causal machine learning, with a track record of setting technical direction beyond a single team.
  • •Experience leading the design and productionization of causal models, measurement platforms, experimentation systems, or large-scale decision engines.
  • •Strong judgment about randomized experiments, observational methods, surrogate endpoints, and model-based decisioning, including when evidence is insufficient.
  • •Fluency with causal methods such as doubly robust estimation, double machine learning, instrumental variables, difference-in-differences, synthetic controls, variance reduction, heterogeneous treatment effects, contextual bandits, and off-policy evaluation.
  • •Strong ML engineering and systems ability to shape data contracts, modeling pipelines, evaluation frameworks, serving patterns, and monitoring for high-stakes production use.
Experience:10+ yearsMarketplacesExperimentationCausal machine learning
Skills:Technical leadershipCross-functional influenceCoachingCausal reasoningEvidence-based decisioning
Languages:English
Tech Stack:Causal inferenceEconometricsExperimentationCausal machine learningDoubly robust estimationDouble machine learningInstrumental variablesDifference-in-differencesSynthetic controlsVariance reductionHeterogeneous treatment effectsContextual banditsOff-policy evaluationTreatment effect estimationSurrogate validationCounterfactual policy evaluationSensitivity analysisSequential learning

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