Running experiments on marketing channels involves many challenges, yet at DoorDash, we found a number of ways to optimize our marketing with rigorous testing on our digital ad platforms. While data scientists frequently run experiments, such as A/B tests, on new features, the methodology and results may not seem so clear when applied to digital ...
DoorDash extended its machine learning platform to support ensemble models.
DoorDash recaps a number of its engineering highlights from 2020, including its microservices architecture, data platform, and new frontend development.
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 ...
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 ...
Learn how we analyzed over 100K online delivery menus to develop menu best practices
DoorDash engineering explains how to edit large data tables safely and quickly in a production database.
To speed up the development of new features we needed a way to increase our experiment capacity. Learn how we improved it by 4X
Learn how moving ML models to a prediction service can free up RAM and CPU for more scalable development
Learn the challenges and best practices to successfully growing a data platform organization
DoorDash engineers built Curie, a new experimentation analysis platform, to better gauge the success of product experiments.
Learn the challenges of reducing network overheads with gRPC optimizations