Machine Learning Engineer, Supply Strategy & Optimization

About the Team

The Supply Strategy and Optimization team ensures Door Dash’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 Door Dash.

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.

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 Door Dash 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.

• 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 Door Dash.

• 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.

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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…