Principal Machine Learning Engineer, TEAM
DoorDash USA · San Francisco, CA; Sunnyvale, CA; Seattle, WA; New York, NY
About this role
**Principal Machine Learning Engineer — TEAM** ## About the Team DoorDash is building the next generation of **causal decisioning systems** for new verticals including 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’re hiring a **Principal Machine Learning Engineer** to lead the **Causal ML pod** and establish the technical foundation for **company-level causal decisioning**. ## About the Role You will set the **multi-year technical direction** for causal ML, lead the pod’s portfolio and operating model, and stay close to the hardest modeling and systems work—accountable for both **scientific credibility** and **production impact**. ## You’ll Do - Lead the Causal ML pod across **technical strategy, architecture, execution, and quality**; create a roadmap connecting platform foundations to high-value product applications. - Define a **company-level causal value metric** and its measurement framework (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 how **randomized experiments, quasi-experiments, observational estimation, and learned models** work together, and make 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** so causal estimates remain reliable as policies and marketplace conditions change. - Influence senior leaders across **Product, Engineering, Analytics, Finance, Strategy, and business teams** by translating causal evidence into clear decisions and tradeoffs. - Develop senior engineers and scientists through technical direction, design review, coaching, and high standards for causal reasoning and engineering craft. ## Example Focus Areas - **Company-level causal value:** A common, causally grounded measure of long-term value generated by product and business actions. - **Consumer action & lifetime value:** Interventions that grow durable customer value (not just pull demand forward). - **Surrogate metrics & faster learning:** Early indicators that accelerate decisions before long-term outcomes mature. - **Counterfactual ranking & decisioning:** Causal layers for recommendations, search, targeting, demand shaping, and marketplace allocation. - **Causal measurement platform:** Shared infrastructure connecting experiments, observational data, policy logs, estimation, calibration, and decision workflows. ## We’re Excited About You Because You Have - **10+ years** experience in causal inference, econometrics, experimentation, or causal machine learning, including setting technical direction beyond a single team. - Experience designing and productionizing **causal models, measurement platforms, experimentation systems, or la
Listing freshness
CronJobs last confirmed this listing 2h ago. If its source stops confirming the opening for seven days, this page is removed from active inventory.