Engineering Manager, Machine Learning Platform

About the position

Come help us build the world's most reliable on-demand, logistics engine for delivery! We're bringing on talented engineers to help us create and maintain a 24x7, no downtime, global infrastructure system that powers DoorDash’s three-sided marketplace of consumers, merchants, and dashers. DoorDash is a machine learning driven organization and relies on machine learning to improve customer experience, power many business and product decisions, and to reduce cost. The Machine Learning Platform owns all the infrastructure necessary to enable DoorDash engineers to quickly and efficiently apply machine learning. The platform covers the entire ML development lifecycle, which includes featuring engineering, feature store, model store, model training, model inference and ML observability, and more.

Responsibilities

• Manage the redesign and operations of the Feature Store and broader Feature Platform.
• Lead a high-performing team of engineers, driving both the technical vision and execution.
• Work closely with ML managers, product teams, and other platform teams to understand business needs and align priorities.
• Deliver a robust platform that accelerates ML development and improves model performance.
• Shape the future of the Feature Platform by defining the technical strategy and driving key architectural decisions.
• Solve complex engineering challenges at scale, including low-latency feature serving, data consistency, and real-time feature updates.
• Mentor and grow a team of talented engineers, creating a strong culture of technical excellence and professional development.

Requirements

• 8+ years of industry experience in software engineering, machine learning, or infrastructure.
• 2+ years of experience in an engineering management role, leading teams focused on building infrastructure or platform solutions.
• Experience building and operating feature stores or ML platforms.
• Deep interest in machine learning and MLOps, with a strong understanding of how ML models are built, deployed, and maintained.
• Hands-on experience with machine learning infrastructure in a cloud environment.
• Proficiency in cloud-based environments such as AWS, GCP, or Azure.

Nice-to-haves

• Experience with data processing or distributed systems.

Benefits

• 401(k) plan with an employer match
• Paid time off
• Paid parental leave
• Wellness benefits
• Paid holidays
• Medical, dental, and vision benefits
• Disability and basic life insurance
• Family-forming assistance
• Commuter benefit match
• Mental health program