Principal ML Engineer - Causal Value & Decisioning

DoorDash; New York (NY); On-site; USD 282,000 - 415,000; Full time

Benefits offered by this job

401(k) matching

Parental leave (16 weeks)

Wellness benefits

Commuter benefits match

Paid time off

Paid sick leave

Medical/dental/vision benefits

Disability insurance

Life insurance

Family-forming assistance

Mental health program

Job summary

DoorDash is hiring a Principal Machine Learning Engineer to lead the Causal ML pod and build the company-level causal decisioning platform. You will drive multi-year technical direction, ensure scientific credibility, and deliver production-ready causal capabilities across promotions, discovery, and ranking.

You will collaborate with senior leaders across Product, Engineering, Analytics, Finance, and Strategy to translate complex causal evidence into actionable decisions, shaping the long-term

Qualifications

• 10+ years in causal inference, econometrics, experimentation, or causal ML with broad technical direction experience.
• Experience leading design and productionization of causal models, measurement platforms, or large-scale decision engines.
• Strong judgment on randomized experiments, observational methods, surrogate endpoints, and model-based decisioning.

Responsibilities

• Lead the Causal ML pod across strategy, architecture, execution, and quality.
• Define the company-level causal value metric and its measurement framework.
• Build the metric into a decision system for prioritization, experiments, and portfolio tradeoffs.
• Architect reusable causal capabilities for treatment effects, surrogate validation, and counterfactual evaluation.
• Guide production applications across promotions, lifecycle interventions, ranking, and discovery.
• Set validation, monitoring, reproducibility, and governance standards for causal estimates.

Skills

Causal inference

Econometrics

Experimentation

Causal ML

ML engineering

Job description

DoorDash is hiring a Principal Machine Learning Engineer to lead the Causal ML pod and build the company-level causal decisioning platform. You will drive multi-year technical direction, ensure scientific credibility, and deliver production-ready causal capabilities across promotions, discovery, and ranking.

You will collaborate with senior leaders across Product, Engineering, Analytics, Finance, and Strategy to translate complex causal evidence into actionable decisions, shaping the long-term