Causal ML Engineer: Uplift & Counterfactual Decisioning

Visa Hunt; San Francisco, Sunnyvale (CA, CA); On-site; USD 204,000 - 299,000; Full time

Benefits offered by this job

401(k) plan with employer matching

Paid parental leave

Wellness benefits

Commuter benefits match

Job summary

DoorDash is hiring a Causal Machine Learning Engineer to build the causal ML foundation behind growth for New Verticals. This role focuses on production causal systems, uplift models, and counterfactual evaluation to inform ranking, recommendations, and marketplace interventions.

You will work with ML engineers, economists, data scientists, product managers, and leaders to connect experimentation with ML decisioning, driving faster, more reliable decisions across the platform.

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.
• Strong judgment around the tradeoffs between randomized experiments, observational estimation, and model-based decisioning.

Responsibilities

• Design, build, and productionize causal ML systems that influence real marketplace decisions across New Verticals.
• Build uplift / heterogeneous treatment effect models for consumer lifecycle value, promotions, retention, and reactivation.
• Develop counterfactual evaluation frameworks for ranking, recommendations, search, promotions, substitutions, and marketplace interventions.
• Connect experimentation, observational data, and ML decisioning to help teams make better tradeoffs when randomized experiments are slow, noisy, or incomplete.
• Translate causal models into production systems that can shape decisions in ranking, targeting, budget allocation, inventory-aware discovery, and consumer growth.
• Raise the bar for causal reasoning across ML teams: when to trust a model, when not to, and how to debug causal claims in a real marketplace.

Skills

causal inference

econometrics

experimentation

production ML systems

Job description

DoorDash is hiring a Causal Machine Learning Engineer to build the causal ML foundation behind growth for New Verticals. This role focuses on production causal systems, uplift models, and counterfactual evaluation to inform ranking, recommendations, and marketplace interventions.

You will work with ML engineers, economists, data scientists, product managers, and leaders to connect experimentation with ML decisioning, driving faster, more reliable decisions across the platform.