Staff Machine Learning Engineer, Fulfillment Planning

DoorDash is a technology and logistics company focused on empowering local economies. They are seeking 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.

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

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