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About the Role

We are hiring a Causal Machine Learning Engineer to help build the causal ML foundation behind how DoorDash grows New Verticals. This role involves designing, building, and productionizing causal ML systems that influence real marketplace decisions, building uplift/heterogeneous treatment effect models, and developing counterfactual evaluation frameworks. You will join a small, senior pod of causal ML and econometrics experts working across ML, Analytics, Product, and Engineering to build the causal spine for a large-scale consumer marketplace.

Responsibilities

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.

Requirements

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 engineering ability: you can build reliable pipelines, train models, evaluate them rigorously, and partner with platform teams to put them into production.
Strong product judgment: you can connect methods to business decisions, not just optimize offline metrics.
The ability to operate across functions with ML engineers, economists, data scientists, product managers, and business leaders.

Skills

Causal inference
Econometrics
Experimentation
Causal ML
Uplift models
Heterogeneous treatment effect models
Surrogate metrics
Experimentation platforms
Counterfactual policy evaluation
Promotion optimization
Marketplace decisioning systems
Doubly robust estimation
IV
Diff-in-diff
Synthetic controls
Double ML
CUPED-style variance reduction
Contextual bandits
Off-policy evaluation
ML engineering
Product judgment

Location

United States
Illinois
Colorado
Remote

Work Type

Full-time

Experience Level

Senior

Salary/Compensations

$203,500—$299,300 USD

Benefits

401(k) plan with employer matching
16 weeks of paid parental leave
Wellness benefits
Commuter benefits match
Paid time off
Paid sick leave
Medical benefits
Dental benefits
Vision benefits
11 paid holidays
Disability and basic life insurance
Family-forming assistance
Mental health program
Flexible paid time off/vacation for salaried roles
80 hours of paid sick time per year for salaried roles

About the Company

DoorDash is building the next generation of causal decisioning systems for New Verticals: grocery, convenience, retail, alcohol, pets, flowers, and other emerging categories.
At DoorDash, our mission to empower local economies shapes how our team members move quickly, learn, and reiterate in order to make impactful decisions that display empathy for our range of users—from Dashers to merchant partners to consumers.
We are a technology and logistics company that started by enabling door-to-door delivery, and we are looking for team members who can help us go from a company that is known as the place you order food to a company that people turn to for any and all goods.
DoorDash is growing rapidly and changing constantly, which gives our team members the opportunity to share their unique perspectives, solve new challenges, and own their careers.
We're committed to supporting employees’ happiness, healthiness, and overall well-being by providing comprehensive benefits and perks including premium healthcare, wellness expense reimbursement, paid parental leave and more.

Equal Opportunity

We’re committed to growing and empowering a more inclusive community within our company, industry, and cities. That’s why we hire and cultivate diverse teams of people from all backgrounds, experiences, and perspectives. We believe that true innovation happens when everyone has room at the table and the tools, resources, and opportunity to excel.
In keeping with our beliefs and goals, no employee or applicant will face discrimination or harassment based on: race, color, ancestry, national origin, religion, age, gender, marital/domestic partner status, sexual orientation, gender identity or expression, disability status, or veteran status.
Above and beyond discrimination and harassment based on “protected categories,” we also strive to prevent other subtler forms of inappropriate behavior (i.e., stereotyping) from ever gaining a foothold in our office.
Whether blatant or hidden, barriers to 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.
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.