Staff ML Engineer - Real-Time Fulfillment & Logistics

DoorDash, Inc.; San Francisco, Sunnyvale (CA, CA); On-site; USD 200,000 - 280,000; Full time

Benefits offered by this job

401(k) plan with employer matching

Paid parental leave (16 weeks)

Wellness benefits

Commuter benefits match

Paid time off & sick leave

Job summary

DoorDash, Inc. is seeking a Staff Machine Learning Engineer to lead the design, development, and deployment of large-scale production ML systems powering real-time decisions in 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 is a high-impact, Staff-level IC role; you will define architectures, set deployment standards, mentor

Qualifications

• 8+ years of industry experience building and deploying production-scale machine learning systems.
• Strong ML fundamentals and ability to apply them to large-scale production systems.
• Fluent in Python and hands-on with modern ML frameworks (e.g., deep learning).
• Experience designing, launching, and operating mission-critical ML models in production.

Responsibilities

• Own and design large-scale production ML systems for real-time decisioning in logistics.
• Lead 0→1 ML initiatives across fulfillment products and teams.
• Define architectures, deployment standards, and governance for ML systems.
• Mentor engineers and influence technical direction across multiple teams.

Skills

Python

Machine Learning

Production systems

Experimentation

Communication

Tools

TensorFlow

PyTorch

Scikit-learn

Job description

DoorDash, Inc. is seeking a Staff Machine Learning Engineer to lead the design, development, and deployment of large-scale production ML systems powering real-time decisions in 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 is a high-impact, Staff-level IC role; you will define architectures, set deployment standards, mentor