Principal Machine Learning Engineer, TEAM

DoorDash USA•Sunnyvale, CA

1d•$282,100 - $414,800

About The Position

DoorDash is building the next generation of causal decisioning systems for New Verticals, including grocery, convenience, retail, alcohol, pets, and flowers. These businesses operate in dynamic marketplaces where every consumer, merchant, item, promotion, substitution, search result, and delivery promise presents a causal question. This Principal Machine Learning Engineer role will lead the Causal ML pod, establishing the technical foundation for company-level causal decisioning. It is a senior technical leadership position for an individual who has experience building consequential causal systems in production and can translate ambiguous business questions into a coherent measurement and decision platform. A key responsibility is to define and build a durable company-level causal value metric, serving as a trusted, long-term signal to estimate the incremental value created by product, growth, and marketplace actions. This metric will integrate experiments, observational evidence, and production ML, enabling leaders and product teams to compare investments on a common basis while safeguarding customer experience and marketplace health.

Requirements

Extensive experience (10+ years) in causal inference, econometrics, experimentation, or causal machine learning, with a 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.
Deep judgment about randomized experiments, observational methods, surrogate endpoints, and model-based decisioning, including recognizing when evidence is insufficient for a decision.
Fluency with 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, including shaping data contracts, modeling pipelines, evaluation frameworks, serving patterns, and monitoring for high-stakes production use.
The ability to reason about long-term customer and marketplace value, not only local model metrics or immediate conversion.
A track record of influencing executives and senior cross-functional partners through clear problem framing, technical judgment, and evidence.
A multiplier mindset: creating reusable abstractions, improving decision quality across teams, and raising the technical standard of people around you.

Responsibilities

Lead the Causal ML pod across technical strategy, architecture, execution, and quality, creating a roadmap that integrates foundational platform work with high-value product applications.
Define the company-level causal value metric and its measurement framework, including the target construct, time horizon, component outcomes, 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 the integration of randomized experiments, quasi-experiments, observational estimation, and learned models, making the limits of each evidence source explicit.
Architect reusable causal capabilities for treatment effect estimation, surrogate validation, counterfactual policy evaluation, sensitivity analysis, and long-term outcome forecasting.
Guide production applications across promotions, lifecycle interventions, ranking, recommendations, search, substitutions, demand shaping, and inventory-aware discovery.
Set standards for validation, monitoring, reproducibility, and governance to ensure causal estimates remain reliable amidst changing policies, populations, and marketplace conditions.
Influence senior leaders across Product, Engineering, Analytics, Finance, Strategy, and business teams by translating complex causal evidence into clear decisions and tradeoffs.
Develop senior engineers and scientists through technical direction, design review, coaching, and maintaining a high bar for causal reasoning and engineering craft.
Focus on company-level causal value: Create a common, causally grounded measure of long-term value generated by product and business actions, enabling teams to compare opportunities across surfaces while preserving interpretable components and guardrails.
Focus on consumer action and lifetime value: Estimate interventions that truly grow durable customer value, not just pull demand forward, including promotions, lifecycle nudges, personalization, retention, and reactivation.
Focus on surrogate metrics and faster learning: Develop and validate early indicators to accelerate decisions before long-term outcomes mature, paired with variance reduction, sequential learning, and clear standards for trust.
Focus on counterfactual ranking and decisioning: Build causal layers for recommendations, search, targeting, demand shaping, and marketplace allocation to optimize incremental outcomes beyond predictive relevance.
Focus on causal measurement platform: Create shared infrastructure connecting experiments, observational data, policy logs, estimation, calibration, and decision workflows across teams.