Staff ML Engineer - Real-Time Logistics & Optimization

DoorDash; California (MO); On-site; USD 137,000 - 299,000; Full time

Benefits offered by this job

401(k) with employer matching

Paid parental leave (16 weeks)

Wellness benefits

Commuter benefits match

Paid time off

Paid sick leave

Medical, dental, and vision benefits

Job summary

DoorDash is seeking a Staff Machine Learning Engineer to lead the design, development, and deployment of large-scale production ML systems for real-time decisioning across the fulfillment ecosystem. You will own ML systems for assignment and fulfillment estimation, partnering with Product, Data Science, Engineering, and Platform teams to improve delivery quality, cost, and efficiency.

This high-impact role involves defining architectures, setting modeling and deployment standards, mentoring

Qualifications

• 8+ years of industry experience building and deploying production-scale machine learning systems.
• Strong machine learning fundamentals and ability to apply them to large-scale production systems.
• Fluent in Python, with hands-on experience in modern ML frameworks.
• Designed, launched, and operated mission-critical ML models in production including monitoring and governance.

Responsibilities

• Own and build foundational ML systems that impact delivery quality and logistics efficiency.
• Work on real-time assignment, routing, and fulfillment estimation problems.
• Lead 0→1 ML initiatives, defining ML/optimization use across fulfillment products.
• Mentor engineers and raise the technical bar for logistics ML across the organization.

Skills

Python

Production ML systems

ML frameworks

Knowledge distillation

Job description

DoorDash is seeking a Staff Machine Learning Engineer to lead the design, development, and deployment of large-scale production ML systems for real-time decisioning across the fulfillment ecosystem. You will own ML systems for assignment and fulfillment estimation, partnering with Product, Data Science, Engineering, and Platform teams to improve delivery quality, cost, and efficiency.

This high-impact role involves defining architectures, setting modeling and deployment standards, mentoring