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Principal Machine Learning Engineer, Causal Decisioning

DoorDash USA · San Francisco · Office location (mode not stated)

USD 282,100–414,800 / year

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Company
DoorDash USA
Location
San Francisco, CA; Sunnyvale, CA; Seattle, WA; New York, NY
Work mode
Office location (mode not stated)
Open to applicants from
On-site/hybrid: local candidates
Level
Lead / manager & above
Employment
Not stated
Field
Data & AI / Machine learning & AI
Stated pay
USD 282,100–414,800 / year
Posted
2026-09-16 (2 months ago)
Visa sponsorship mentioned
Not mentioned
Listing source
Greenhouse

Machine learning AI / ML engineering & research

About the role

About the Team DoorDash is building the next generation of causal decisioning systems for New Verticals: grocery, convenience, retail, alcohol, pets, flowers, and other emerging categories. These businesses operate in high-dimensional, dynamic marketplaces where every consumer, merchant, item, promotion, substitution, search result, and delivery promise creates a causal question. We are hiring a Principal Machine Learning Engineer to lead the Causal ML pod and establish the technical foundation for company-level causal decisioning. This is a senior technical leadership role for a practitioner who has built consequential causal systems in production and can turn ambiguous business questions…

Excerpt only. Read the full description and apply at the original posting on Greenhouse. We list jobs from public job-board feeds and do not alter employer content beyond this excerpt.

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