Staff Machine Learning Engineer, Fulfillment Planning
About The Position
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. 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.
Requirements
- 8+ years of industry experience building and deploying production-scale machine learning systems.
- Strong machine learning fundamentals and know how to apply them to large-scale production systems.
- Fluent in Python
- Hands-on experience with modern ML frameworks, especially deep learning frameworks.
- Designed, launched, and operated mission-critical ML models or systems in production, including monitoring, retraining, reliability, and governance.
- Can lead complex technical projects end to end and influence stakeholders across multiple teams or organizations.
- Communicate clearly with both technical and non-technical audiences.
- Comfortable operating in ambiguous problem spaces and turning 0→1 ideas into production systems.
- 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.
Responsibilities
- 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.
Benefits
- 401(k) plan with employer matching
- 16 weeks of paid parental leave
- Wellness benefits
- Commuter benefits match
- Paid time off
- Paid sick leave
- Medical, dental, and vision benefits
- 11 paid holidays
- Disability and basic life insurance
- Family-forming assistance
- Mental health program
- Equity grants
Stand Out From the Crowd
Upload your resume and get instant feedback on how well it matches this job.
What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
