Senior Causal ML Engineer — Value Metrics & Decisioning

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

Benefits offered by this job

Equity grants

Comprehensive benefits package

401(k) with employer matching

Job summary

DoorDash is seeking a Principal Machine Learning Engineer to lead the Causal ML pod and define a durable company-level causal value metric. You will steer multi-year technical direction, ensure scientific credibility, and drive production impact across promotions, lifecycle interventions, ranking, and search.

You will translate advanced causal evidence into strategic decisions and build reusable, scalable causal capabilities.

Qualifications

• 10+ years of experience in causal inference, econometrics, experimentation, or causal ML.
• 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.

Responsibilities

• Set the multi-year technical direction for causal ML and lead the pod’s portfolio.
• Define the company-level causal value metric and its measurement framework.
• Build the metric into a decision system for product prioritization, experiments, and budget allocation.
• Architect reusable causal capabilities for treatment estimation, counterfactual evaluation, and long-term forecasting.
• Guide production applications across promotions, lifecycle interventions, ranking, and search.

Skills

Causal inference

Econometrics

Experimentation

Causal ML

Technical leadership

Production ML

Model deployment

Experiment design

Job description

DoorDash is seeking a Principal Machine Learning Engineer to lead the Causal ML pod and define a durable company-level causal value metric. You will steer multi-year technical direction, ensure scientific credibility, and drive production impact across promotions, lifecycle interventions, ranking, and search.

You will translate advanced causal evidence into strategic decisions and build reusable, scalable causal capabilities.