Data
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 . Over the course of our interviews, I’ve come across some great candidates who ...
Read More Lokesh Bisht
Data Machine Learning
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 ...
Read More Arbaz Khan
Data Machine Learning
Learn how we analyzed over 100K online delivery menus to develop menu best practices
Read More Finn Qiao
Data
DoorDash engineering explains how to edit large data tables safely and quickly in a production database.
Read More Justin Lee
Data Machine Learning
To speed up the development of new features we needed a way to increase our experiment capacity. Learn how we improved it by 4X
Read More Jessica Zhang
Backend Data
Learn how moving ML models to a prediction service can free up RAM and CPU for more scalable development
Read More Sarah Chen
Data Machine Learning
Learn the challenges and best practices to successfully growing a data platform organization
Read More Sudhir Tonse
Data Machine Learning
DoorDash engineers built Curie, a new experimentation analysis platform, to better gauge the success of product experiments.
Read More Arun Balasubramani
Backend Data Machine Learning
Learn the challenges of reducing network overheads with gRPC optimizations
Read More Arbaz Khan
Data Machine Learning
Learn how building a prediction service enables the utilization of ML models based on real-time data
Read More Cody Zeng
