Staff Causal ML Engineer for Marketplace Growth

DoorDash, Inc.; San Francisco (CA); On-site; USD 204,000 - 299,000; Full time

Benefits offered by this job

401(k) with employer matching

16 weeks paid parental leave

Wellness benefits

Commuter benefits match

Paid time off

Job summary

DoorDash, Inc. is hiring a Staff Machine Learning Engineer focused on causal inference. You will design and productionize causal ML systems that drive New Verticals, including uplift models, counterfactual evaluation, and budget-aware decisioning across a large-scale marketplace.

You will collaborate with ML engineers, economists, and product teams to align methods with business goals, ensuring robust causal claims and scalable production pipelines.

Qualifications

• Deep practical experience with causal inference, econometrics, experimentation, or causal ML.
• Experience shipping models or decision systems in production, ideally in consumer marketplaces, ads, recommendations, search, pricing, promotions, logistics, fintech, or other high-scale settings.

Responsibilities

• Design, build, and productionize causal ML systems that influence marketplace decisions.
• Build uplift / heterogeneous treatment effect models for consumer value, promotions, retention, and reactivation.
• Develop counterfactual evaluation frameworks for ranking, recommendations, and promotions.
• Translate causal models into production systems that shape ranking, targeting, and budget allocation.

Skills

Causal inference

Experimentation

Production ML

Econometrics

Tools

uplift models

IV

diff-in-diff

Job description

DoorDash, Inc. is hiring a Staff Machine Learning Engineer focused on causal inference. You will design and productionize causal ML systems that drive New Verticals, including uplift models, counterfactual evaluation, and budget-aware decisioning across a large-scale marketplace.

You will collaborate with ML engineers, economists, and product teams to align methods with business goals, ensuring robust causal claims and scalable production pipelines.