Staff Machine Learning Engineer, Fulfillment Planning

DoorDash connects customers with local businesses for delivery, from restaurants to grocery, retail and its own DashMart stores. The team works on some of DoorDash’s most important logistics systems, including:.

Key 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

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
• Lead complex technical projects end to end and influence stakeholders across multiple teams or organizations

Location and schedule

• Onsite in San Francisco, California

Salary and benefits

US base salary $203,500 to $299,300 a year. DoorDash also offers benefits; the full list is on the posting.

How to apply

Apply on the company's own site.