Principal Machine Learning Engineer, TEAM

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About the role

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, dynamic marketplaces where every consumer, merchant, item, promotion, substitution, search result, and delivery promise creates a causal question. We are hiring a Principal Machine Learning Engineer to lead the Causal ML pod and establish the technical foundation for company-level causal decisioning. This is a senior technical leadership role for a practitioner who has built consequential causal systems in production and can turn ambiguous business questions into a coherent measurement and decision platform. A central mandate is to define and build a durable company-level causal value metric: a trusted, long-term signal that estimates the incremental value created by product, growth, and marketplace actions. The metric will connect experiments, observational evidence, and production ML so leaders and product teams can compare investments on a common basis while protecting customer experience and marketplace health. About the Role You will set the multi-year technical direction for causal ML, lead the pod’s portfolio and operating model, and remain close to the hardest modeling and systems work. You will be accountable for both scientific credibility and production impact. You’re excited about this opportunity because you will… • Lead the Causal ML pod across technical strategy, architecture, execution, and quality. Create a roadmap that joins 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 that teams can use 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. Make the limits of each source of evidence 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 so causal estimates remain reliable as policies, populations, and marketplace conditions change. • 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 a high bar for causal reasoning and engineering craft. Example focus areas include: • Company-level causal value : Create a common, causally grounded measure of the long-term value generated by product and business actions, enabling teams to compare opportunities across surfaces while preserving interpretable components and guardrails; • Consumer action and lifetime value : Estimate which interventions truly grow durable customer value rather than pull demand forward, including promotions, lifecycle nudges, personalization, retention, and reactivation; • Surrogate metrics and faster learning : Develop and validate early indicators that accelerate decisions before long-term outcomes mature, paired with variance reduction, sequential

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