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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 engine, the team focused largely on reliability.

The Undervalued Skills Candidates Need to Succeed in Data Science Interviews

After interviewing over a thousand candidates for Data Science roles at DoorDash and only hiring a very small fraction, I have come to realize that any interview process is far from perfect, but there are often strategies to improve one’s chances .

Future-proofing: How DoorDash Transitioned from a Code Monolith to a Microservice Architecture

In 2019, DoorDash’s engineering organization initiated a process to completely reengineer the platform on which our delivery logistics business is based.

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.