DoorDash
Technology
StaffMachineLearningEngineer,FulfillmentPlanning
Neural analysis suggests this role is
optimal for Staff candidates.
“Staff Machine Learning Engineer, Fulfillment Planning at DoorDash. Skills: Machine Learning, Production Systems, Logistics Optimization, Architecture. Lead design, development, deployment of ML systems. Own ML systems for assignment, fulfillment estimation”
What You'll Achieve.
Improve customer experience; Improve merchant outcomes; Improve Dasher efficiency; Improve DoorDash profitability; Improve fulfillment quality; Reduce fulfillment cost; Improve delivery quality; Improve delivery cost; Improve logistics efficiency
Industry & Context.
Operating in ambiguous problem spaces; Turning 0→1 ideas into production systems
What They're Looking For.
Must Have
8+ years of industry experience building and deploying production-scale machine learning systems, Machine learning fundamentals, Fluent in Python, Hands-on experience with modern ML frameworks, Designed, launched, and operated mission-critical ML models or systems in production, Lead complex technical projects end to end, Communicate clearly with both technical and non-technical audiences, Comfortable operating in ambiguous problem spaces, Built or shipped large-scale ML models for recommendation, ads, marketplace, logistics, or other domains, Experience with knowledge distillation from large teacher models into efficient production models
Nice to Have
Deep learning frameworks
What You'll Do.
deployment of ML systems
Own ML systems for assignment
fulfillment estimation
Improve delivery quality
Contribute to batching
fulfillment execution
Set modeling standards
Set deployment standards
Mentor other engineers
Shape ML application in logistics
Define AI vision for logistics
Build foundational ML systems
How You'll Work.
Team & Collaboration
Partnering closely with Product, Data Science, Engineering, Platform teams; Collaborate closely with Product, Data Science, Platform Engineering; Highly cross-functional environment
Communication Scope
Communicate clearly with both technical and non-technical audiences
Process & Methodology
Lead complex technical projects end to end
Full Job Description
About the Team The Fulfillment Planning team builds the intelligence that powers DoorDash’s logistics network. We optimize how deliveries are planned and executed across the full delivery lifecycle, improving customer experience, merchant outcomes, Dasher efficiency, and DoorDash profitability. Our mission is to improve fulfillment quality while reducing fulfillment cost. We do this by applying machine learning, optimization, and systems engineering to the core decisions behind assignment, routing, batching, timing, and fulfillment estimation. The team works on some of DoorDash’s most important logistics systems, including: The core assignment engine that matches deliveries with Dashers in real time. Real-time ETA and fulfillment estimation systems for consumers, Dashers, and merchants across diverse geographies and all business lines. Assignment and planning algorithms for specialized delivery types, including grocery, retail, parcel, and catering. ML models and optimization algorithms that shape demand, improve service quality, and reduce cost. Tier-0 logistics services that require high reliability, low latency, and strong operational discipline. The team also builds reusable ML systems and modeling patterns that scale across DoorDash’s logistics ecosystem. This role will help define the technical direction and best practices for logistics ML at DoorDash. About the Role We’re looking for a Staff Machine Learning Engineer to lead the design, development, and deployment of large-scale production ML systems that drive real-time decisioning across DoorDash’s fulfillment ecosystem. You will start by owning ML systems for assignment and fulfillment estimation, partnering closely with Product, Data Science, Engineering, and Platform teams to improve delivery quality, cost, and efficiency. Over time, you may also contribute to adjacent areas such as batching, fulfillment execution, demand shaping, and logistics optimization across DoorDash’s business lines. This is a hig
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