Staff Machine Learning Engineer, Causal Inference

The posting, as DoorDash wrote it

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, messy marketplaces where every consumer, merchant, item, promotion, substitution, search result, and delivery promise creates a causal question.

About the Role

We are hiring a Causal Machine Learning Engineer to help build the causal ML foundation behind how DoorDash grows New Verticals. We are looking for someone who has built or deeply worked on production causal systems: uplift models, heterogeneous treatment effect models, surrogate metrics, experimentation platforms, counterfactual policy evaluation, promotion optimization, or marketplace decisioning systems.

You will join a small, senior pod of causal ML and econometrics experts working across ML, Analytics, Product, and Engineering. The mandate is to build the causal spine for a large-scale consumer marketplace.

You're excited about this opportunity because you will…

• 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.
• Build systems that connect experimentation, observational data, and ML decisioning so teams can make better tradeoffs when randomized experiments are slow, noisy, or incomplete.
• Design surrogate metrics and early indicators that help teams move faster while preserving long-term marketplace health.
• Partner with econometrics and analytics leaders to choose the right methods: doubly robust estimation, IV, diff-in-diff, synthetic controls, double ML, CUPED-style variance reduction, contextual bandits, off-policy evaluation, and related approaches.
• 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.

We're excited about you because you have…

• 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.
• Comfort debating and applying methods such as doubly robust estimation, double ML, IV, diff-in-diff, CUPED, uplift modeling, contextual bandits, and off-policy evaluation .
• Strong ML en

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success have no place at DoorDash. We value a diverse workforce – people who identify as women, non-binary or gender non-conforming, LGBTQIA+, American Indian or Native Alaskan, Black or African American, Hispanic or Latinx, Native Hawaiian or Other Pacific Islander, differently-abled, caretakers and parents, and veterans are strongly encouraged to apply. Thank you to the Level Playing Field Institute for this statement of non-discrimination.

Pursuant to the San Francisco Fair Chance Ordinance, Los Angeles Fair Chance Initiative for Hiring Ordinance, and any other state or local hiring regulations, we will consider for employment any qualified applicant, including those with arrest and conviction records, in a manner consistent with the applicable regulation.

If you need any accommodations, please inform your recruiting contact upon initial connection.

Notice Regarding Use of AI and Automated Tools: To streamline our hiring process, DoorDash utilizes an automated recruitment tool called Metaview.

How it works: Metaview assists our recruiting team by evaluating job related qualifications and characteristics in connection with hiring. The tool is designed and used to support - rather than replace - human decision-making; trained personnel make final decisions with meaningful human review and oversight, and DoorDash does not use Metaview or other AI-enabled tool in a manner that has the effect of subjecting applicants or employees to discrimination based on any protected characteristic or proxy or for engaging in any protected activity under applicable law.

Data Retention, Privacy & Bias Audit: Data collected during this process is retained in accordance with our Candidate Privacy Policy https://help.doordash.com/en-us/legal/article/ax-privacy-notice and applicable state laws. In compliance with New York City Local Law 144, the independent bias audit summary for Metaview is publicly available for review at https://trust.warden-ai.com/metaview/application-review .