Machine Learning

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Culture Machine Learning Wanted: Data Scientists with Technical Brilliance AND Business Sense

DoorDash seeks data scientists who prioritize the business impacts of their work.

Alok Gupta

13 Minute Read

Backend Culture Data General Machine Learning Mobile Web

2020 Hindsight: Building Reliability and Innovating at DoorDash

DoorDash recaps a number of its engineering highlights from 2020, including its microservices architecture, data platform, and new frontend development.

Things Not Strings: Understanding Search Intent with Better Recall

For every growing company using an out-of-the-box search solution there comes a point when the corpus and query volume get so big that developing a system to understand user search intent is needed to consistently show relevant results. We ran into a similar problem at DoorDash where, after we set up a basic “out-of-the-box” search ...

Iterating Real-time Assignment Algorithms Through Experimentation

DoorDash operates a large, active on-demand logistics system facilitating food deliveries in over 4,000 cities. When customers place an order through DoorDash, they can expect it to be delivered within an hour. Our platform determines the Dasher, our term for a delivery driver, most suited to the task and offers the assignment. Complex real-time logistic ...

Building a Gigascale ML Feature Store with Redis, Binary Serialization, String Hashing, and Compression

When a company with millions of consumers such as DoorDash builds machine learning (ML) models, the amount of feature data can grow to billions of records with millions actively retrieved during model inference under low latency constraints. These challenges warrant a deeper look into selection and design of a feature store — the system responsible ...