About the Team
The Strategy and Supply Optimization team ensures DoorDash’s marketplace remains balanced, efficient, and profitable by intelligently managing Dasher supply and engagement. Our mission is to deliver an exceptional customer experience by keeping roads well-supplied across all geographies and delivery verticals—while enabling Dashers to maximize their earnings and achieve supply outcomes efficiently for DoorDash.
We build systems that forecast supply needs, optimize incentive spend, and enable data-driven decisions across dasher acquisition, retention, and mobilization. The team combines machine learning, optimization, and causal inference to design scalable levers and real-time systems that balance Dasher supply with customer demand across geographies and delivery types.
About the Role
As a Machine Learning Engineer on the team, you’ll design and deploy production ML systems that drive decision-making across Dasher acquisition, incentives, and marketplace balancing.
You’ll own the end-to-end ML lifecycle—from feature engineering and model training to deployment, experimentation, and monitoring—while working closely with partners in Product, Operations, and Analytics to shape how DoorDash optimizes supply and mobilization at scale.
Key initiatives you’ll contribute to:
- Causal inference modeling to measure the incremental impact of Dasher acquisition and incentive strategies.
- Incentive optimization frameworks that personalize pay structures and improve efficiency.
- Budget allocation and forecasting models that identify optimal spend across acquisition, referrals, and retention.
- Platformization of ML systems to standardize forecasting, monitoring, and experimentation at scale
You can find out more on our ML blog here
You’re excited about this opportunity because you will…
- Own and operate ML systems that predict Dasher supply, optimize advertisement spend, and improve marketplace balance.
- Build optimization and causal inference models to improve incentive efficiency and retention.
- Develop automated experimentation pipelines for evaluating incentive performance and marketplace interventions.
- Collaborate cross-functionally to deliver scalable, production-grade ML solutions.
- Advance personalization frameworks that deliver targeted and adaptive Dasher incentives.
- Enhance ML platformization efforts to improve scalability and reliability across use cases.
- Shape the future of supply optimization and Dasher incentives at DoorDash.
We’re excited about you because…
- PhD or 2+ years of industry experience post graduate degree of developing advanced machine learning models with business impact.
- You have hands-on experience owning production ML models and pipelines
- You have strong fundamentals in applied machine learning, optimization, and experiment design
- You thrive in ambiguous, fast-paced environments and are motivated by measurable impact
- You have a track record of collaborating with cross-functional partners and operating with end-to-end ownership
- You’re passionate about using ML to solve high-impact, real-world problem
Notice to Applicants for Jobs Located in NYC or Remote Jobs Associated With Office in NYC Only
We use 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 provide Covey with job requirements and candidate submitted applications. We began using Covey Scout for Inbound from August 21, 2023, through December 21, 2023, and resumed using Covey Scout for Inbound again on June 29, 2024.
The Covey tool has been reviewed by an independent auditor. Results of the audit may be viewed here: Covey
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 ranges for this position within the United States, including Illinois and Colorado.
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 with door-to-door delivery, and we are looking for team members who can help us go from a company that is known for delivering 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.