DoorDash
Machine Learning Engineer, Supply Strategy & Optimization
DoorDash, Sunnyvale, California, United States, 94087
Machine Learning Engineer, Strategy & Supply Optimization
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Machine Learning Engineer, Strategy & Supply Optimization
role at
DoorDash .
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.
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’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.
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.
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.
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Machine Learning Engineer, Strategy & Supply Optimization
role at
DoorDash .
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.
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’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.
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.
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.
#J-18808-Ljbffr