Causal ML Platform Architect & Value Lead

DoorDash, Inc.; San Francisco, Northern (CA, KY); Hybrid; USD 282,000 - 415,000; Full time

Benefits offered by this job

Equity grants

401(k) with employer matching

Paid parental leave

Medical, dental, vision benefits

Paid time off and holidays

Job summary

DoorDash, Inc. is seeking a Principal Machine Learning Engineer to lead the Causal ML pod and define the multi‑year technical direction for company‑level causal decisioning.

You will set the strategic roadmap, build durable causal metrics, and ensure production impact across product and marketplace actions. You will collaborate with senior leaders across Product, Engineering, Analytics, Finance, and Strategy to translate complex causal evidence into clear decisions and scalable systems,

Qualifications

• 10+ years in causal inference, econometrics, experimentation, or causal machine learning
• Experience designing and productionizing causal models or measurement platforms
• Strong judgment on experiments and model-based decisioning
• Fluency with methods like doubly robust estimation, IVs, DiD, synthetic controls
• Strong ML engineering and systems ability

Responsibilities

• Lead the Causal ML pod across strategy, architecture, execution, and quality
• Create a roadmap linking foundational platform work with product applications
• Define and validate a company-level causal value metric and measurement framework
• Build a decision system integrating experiments, observational data, and learned models
• Guide production applications across promotions, lifecycle interventions, and ranking

Skills

Causal inference

Econometrics

Experimentation

Causal ML

ML engineering

Technical leadership

Job description

DoorDash, Inc. is seeking a Principal Machine Learning Engineer to lead the Causal ML pod and define the multi‑year technical direction for company‑level causal decisioning.

You will set the strategic roadmap, build durable causal metrics, and ensure production impact across product and marketplace actions. You will collaborate with senior leaders across Product, Engineering, Analytics, Finance, and Strategy to translate complex causal evidence into clear decisions and scalable systems,