Software Engineer, Machine Learning Infrastructure

B.S., M.S., or PhD. in Computer Science or equivalent

Exceptionally strong knowledge of CS fundamental concepts and OOP languages

6+ years of industry experience in software engineering

Prior experience building machine learning systems in production such as enabling data analytics at scale

Prior experience in machine learning - you've developed and deployed your own models - even if these are simple proof of concepts

Systems Engineering - you've built meaningful pieces of infrastructure in a cloud computing environment. Bonus if those were data processing systems or distributed systems

At DoorDash, our Data Scientists and ML Engineers have the opportunity to dive into a wealth of delivery data to improve company-wide ML workflows such as Search & Recommendations, Dasher Assignment, ETA Prediction, and Dasher Capacity Planning. You will join a small team to build systems that empower efficient machine learning at scale. This is a hybrid opportunity in San Francisco, Sunnyvale or Seattle and the expectation is to work PST hours.

Build a world-class ML platform where models are developed, trained, and deployed seamlessly

Work closely with Data Scientists and Product Engineers to evolve the ML platform as per their use cases

You will help build high performance and flexible pipelines that can rapidly evolve to handle new technologies, techniques and modeling approaches

You will work on infrastructure designs and solutions to store trillions of feature values and power hundreds of billions of predictions a day

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.