Job description

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. This is not a generic ML role with some experimentation work on the side. 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 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. Compensation The successful candidate’s starting pay will fall within the pay range listed below and is determined based on job-related factors including, but not limited to, skills, experience, qualifications, work location, and market conditions. Base salary is localized according to an employee’s work location. Ranges are market-dependent and may be modified in the future. In addition to base salary, the compensation for this role includes opportunities for equity grants. Talk to your recruiter for more information. DoorDash cares about you and your overall well-being. That’s why we offer a comprehensive benefits package to all regular employees, which includes a 401(k) plan with employer matching, 16 weeks of paid parental leave, wellness benefits, commuter benefits match, paid time off and paid sick leave in compliance with applicable laws (e.g. Colorado Healthy Families and Workplaces Act). DoorDash also offers medical, dental, and vision benefits, 11 paid holidays, disability and basic life insurance, family-forming assistance, and a mental health program, among others. To learn more about our benefits, visit our careers page here . See below for paid time off details: For salaried roles: flexible paid time off/vacation, plus 80 hours of paid sick time per year. For hourly roles: vacation accrued at about 1 hour for every 25.97 hours worked (e.g. about 6.7 hours/month if working 40 hours/week; about 3.4 hours/month if working 20 hours/week), and paid sick time accrued at 1 hour for every 30 hours worked (e.g. about 5.8 hours/month if working 40 hours/week; about 2.9 hours/month if working 20 hours/week). The national base pay range for this position within the United States, including Illinois and Colorado. $203,500 — $299,300 USD About DoorDash 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. Our Commitment to Diversity and Inclusion 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. Statement of Non-Discrimination: 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. 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 to Applicants for Jobs Located in NYC or Remote Jobs Associated With Office in NYC Only We used Covey as part of our hiring and/or promotional process for jobs in NYC and certain features may qualify it as an AEDT in NYC. As part of the hiring and/or promotion process, we provided Covey with job requirements and candidate submitted applications. We began using Covey Scout for Inbound from August 21, 2023, through December 21, 2023. We resumed using Covey Scout for Inbound again on June 29, 2024, and ceased using Covey Scout for Inbound on April 30, 2026. The Covey tool has been reviewed by an independent auditor. Results of the audit may be viewed here: getcovey.com . Beware of recruitment scams: DoorDash, Deliveroo, and Wolt will never ask you to pay money or share sensitive financial information during hiring — learn more about our legitimate recruiting process at Recruitment Scam Awareness - DoorDash .

Requirements

Must-haves

About the Team
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.
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 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.
Our Commitment to Diversity and Inclusion
If you need any accommodations, please inform your recruiting contact upon initial connection.
Notice to Applicants for Jobs Located in NYC or Remote Jobs Associated With Office in NYC Only

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Use the role’s language and application details while tailoring your materials.

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machine learningmlgo

Platform: greenhouse
Effort: quick

Benefits

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Dental

Vision

401(K)

Paid Time Off

Parental Leave

Wellness