Senior Causal ML Architect

DoorDash; Seattle (WA); On-site; USD 282,000 - 415,000; Full time

Benefits offered by this job

Equity grants

Medical, dental, and vision benefits

401(k) plan with employer matching

Paid parental leave

Paid time off and sick leave

Job summary

DoorDash is seeking a Principal Machine Learning Engineer to lead the Causal ML pod and establish the technical foundation for company-level causal decisioning. You will set the multi-year direction, own the metric framework, and translate complex causal evidence into clear decisions for product, growth, and marketplace teams.

This senior role blends rigorous research with production impact, guiding experiments, observational evidence, and learned models to optimize value while protecting user

Qualifications

• Extensive experience (10+ years) in causal inference, econometrics, experimentation, or causal machine learning.
• Experience leading the design and productionization of causal models, measurement platforms, experimentation systems, or large-scale decision engines.
• Deep judgment about randomized experiments, observational methods, surrogate endpoints, and model-based decisioning.
• Fluency with methods such as doubly robust estimation, double machine learning, instrumental variables, difference-in-differences, synthetic controls, variance reduction, heterogeneous treatment effects, contextual bandits, and off-policy evaluation.
• Strong ML engineering and systems ability. You can shape data contracts, modeling pipelines, evaluation frameworks, serving patterns, and monitoring for high-stakes production use.
• The ability to reason about long-term customer and marketplace value, not only local model metrics or immediate conversion.
• A track record of influencing executives and senior cross-functional partners through clear problem framing, technical judgment, and evidence.
• A multiplier mindset: you create reusable abstractions, improve decision quality across teams, and raise the technical standard of the people around you.

Responsibilities

• Set multi-year technical direction for causal ML and lead the pod’s portfolio and operating model.
• Be accountable for scientific credibility and production impact of causal systems.
• Define the company-level causal value metric and measurement framework.
• Build the metric into a decision system for prioritization, experiments, and budget allocation.
• Architect reusable causal capabilities for treatment effect estimation and long-term forecasting.
• Guide production applications across promotions, lifecycle interventions, ranking, and discovery.
• Set validation, monitoring, and governance standards to keep estimates reliable over time.
• Influence senior leaders by translating complex causal evidence into clear decisions and tradeoffs.
• Develop senior engineers and scientists through technical direction and design reviews.

Skills

Causal inference

Econometrics

Experimentation

Causal ML

Production ML

ML engineering

Job description

DoorDash is seeking a Principal Machine Learning Engineer to lead the Causal ML pod and establish the technical foundation for company-level causal decisioning. You will set the multi-year direction, own the metric framework, and translate complex causal evidence into clear decisions for product, growth, and marketplace teams.

This senior role blends rigorous research with production impact, guiding experiments, observational evidence, and learned models to optimize value while protecting user