The Staff Machine Learning Engineer will lead the design, development, and deployment of large-scale production machine learning systems at DoorDash. This role is part of the Fulfillment Planning team, which focuses on optimizing logistics through machine learning and systems engineering. The engineer will work closely with Product, Data Science, Engineering, and Platform teams to enhance delivery quality and efficiency. This position is ideal for someone who enjoys building foundational ML systems and influencing technical direction across multiple teams.

• 8+ years of industry experience building and deploying production-scale machine learning systems.
• Strong machine learning fundamentals and the ability to apply them to large-scale production systems.
• Fluency in Python.
• Hands-on experience with modern ML frameworks, especially deep learning frameworks.
• Experience designing, launching, and operating mission-critical ML models or systems in production.

• Own and build foundational ML systems that impact delivery quality and logistics efficiency.
• Work on real-world machine learning problems, including real-time assignment and fulfillment estimation.
• Lead 0→1 ML initiatives and define how machine learning is applied across fulfillment products.
• Influence architecture, strategy, and execution for critical logistics services.
• Establish best practices for model development, deployment, monitoring, retraining, and governance.