Principal Causal ML Architect & Value Metrics Leader

DoorDash Canada; San Francisco (CA); On-site; USD 282,000 - 415,000; Full time

Benefits offered by this job

401(k) with match

Paid parental leave

Health benefits

Job summary

DoorDash is seeking a Principal Machine Learning Engineer to lead the Causal ML pod and establish the technical foundation for company-wide causal decisioning. You will define multi-year strategy, oversee architecture, and ensure production impact with rigorous validation and governance.

You will translate complex causal evidence into actionable decisions for senior leadership, guiding experimentation, measurement, and long-term value assessment across products and marketplace actions.

Qualifications

• Extensive experience (10+ years) in causal inference, econometrics, experimentation, or causal machine learning.
• Experience leading design and productionization of causal models and measurement platforms.
• Deep judgment on randomized experiments, observational methods, surrogate endpoints, and model-based decisioning.

Responsibilities

• Set multi-year technical direction for causal ML and lead the pod’s portfolio and operating model.
• Architect reusable causal capabilities for treatment effect estimation and counterfactual policy evaluation.
• Guide production applications across promotions, lifecycle interventions, ranking, and search with robust validation and governance.
• Influence senior leaders by translating complex causal evidence into clear decisions and tradeoffs.
• Develop engineers and scientists through technical direction and rigorous reviews.

Skills

Causal inference

Experimentation

Causal ML systems

Production ML

Leadership

Job description

DoorDash is seeking a Principal Machine Learning Engineer to lead the Causal ML pod and establish the technical foundation for company-wide causal decisioning. You will define multi-year strategy, oversee architecture, and ensure production impact with rigorous validation and governance.

You will translate complex causal evidence into actionable decisions for senior leadership, guiding experimentation, measurement, and long-term value assessment across products and marketplace actions.