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 high-impact individual contributor role for someone who enjoys building 0→1 ML systems, operating at Staff-level scope, and influencing technical direction across multiple teams. You will define architectures, set modeling and deployment standards, mentor other engineers, and help shape how DoorDash applies machine learning to logistics at scale.

You’re excited about this opportunity because you will…

• Own and build foundational ML systems that directly impact delivery quality, cost, and overall logistics efficiency across DoorDash.
• Work on challenging, real-world machine learning problems, including real-time assignment, routing, and fulfillment estimation.
• Lead 0→1 ML initiatives, defining how machine learning and optimization are applied across fulfillment products.
• Influence architecture, strategy, and execution for a Tier-0 service critical to DoorDash’s logistics platform.
• Collaborate closely with Product, Data Science, and Platform Engineering in a highly cross-functional environment.
• Establish best practices for model development, deployment, monitoring, retraining, and governance.
• Define and lead DoorDash’s cutting-edge AI vision for logistics: an LLM-inspired foundation model for intelligence across logistics
• Mentor other engineers and raise the technical bar for logistics ML across the organization.

We’re excited about you because…

• You have 8+ years of industry experience building and deploying production-scale machine learning systems.
• You have strong machine learning fundamentals and know how to apply them to large-scale production systems.
• You are fluent in Python
• You have hands-on experience with modern ML frameworks, especially deep learning frameworks.
• You have designed, launched, and operated mission-critical ML models or systems in production, including monitoring, retraining, reliability, and governance.
• You can lead complex technical projects end to end and influence stakeholders across multiple teams or organizations.
• You communicate clearly with both technical and non-technical audiences.
• You are comfortable operating in ambiguous problem spaces and turning 0→1 ideas into production systems.
• You have built or shipped large-scale ML models for recommendation, ads, marketplace, logistics, or other domains.
• You have experience with knowledge distillation from large teacher models into efficient production models.