An optimized merchant selection strategy has been one of the key factors that has enabled DoorDash to become an industry leader in US food delivery service.
Tag Archives: machine learning
Using Gamma Distribution to Improve Long-Tail Event Predictions
For DoorDash, being able to predict long-tail events related to delivery times is critical to ensuring consumers’ orders arrive when expected.
How DoorDash Quickly Spins Up Multiple Image Recognition Use Cases
DoorDash has rich image data collected by Dashers, our delivery drivers, that we use in a number of use cases.
Using Triplet Loss and Siamese Neural Networks to Train Catalog Item Embeddings
Understanding the contents of a large digital catalog is a significant challenge for online businesses, but this challenge can be addressed using self-supervised neural network models.
Using ML and Optimization to Solve DoorDash’s Dispatch Problem
DoorDash delivers millions of orders every day with the help of DeepRed, the system at the center of our last-mile logistics platform.
Maintaining Machine Learning Model Accuracy Through Monitoring
Machine learning model drift occurs as data changes, but a robust monitoring system helps maintain integrity.
Building Flexible Ensemble ML Models with a Computational Graph
DoorDash extended its machine learning platform to support ensemble models.
Wanted: Data Scientists with Technical Brilliance AND Business Sense
DoorDash seeks data scientists who prioritize the business impacts of their work.
Iterating Real-time Assignment Algorithms Through Experimentation
DoorDash operates a large, active on-demand logistics system facilitating food deliveries in over 4,000 cities.
Uncovering Online Delivery Menu Best Practices with Machine Learning
Restaurants on busy thoroughfares can use many elements to catch a customer’s eye, but online ordering experiences mostly rely on the menu to generate sales.