At DoorDash, we believe in learning from our marketplace of Consumers, Dashers, and Merchants and thus rely heavily on experimentation to make the data-driven product and business decisions.
Category Archives: AI & ML
Reinforcement Learning for On-Demand Logistics
Overview
Introduction
What is the assignment problem at DoorDash?
How Artificial Intelligence Powers Logistics at DoorDash
In May, DoorDash participated at the O’Reilly Artificial Intelligence Conference in New York where I presented on “How DoorDash leverages AI in its logistics engine.” In this post, I walk you through the core logistics problem at DoorDash and describe how we use Artificial Intelligence (AI) in our logistics engine.
Personalized Store Feed with Vector Embeddings
Customers come to DoorDash to discover and order from a vast selection of their favorite stores, so it is important to be able to surface what is most relevant to them.
Switchback Tests and Randomized Experimentation Under Network Effects at DoorDash
To A/B or not to A/B, that is the question
Overview
On the Dispatch team at DoorDash, we use simulation, empirical observation, and experimentation to make progress towards our goals; however, given the systemic nature of many of our products, simple A/B tests are often ineffective due to network effects.
Powering Search & Recommendations at DoorDash
Customers across North America come to DoorDash to discover and order from a vast selection of their favorite stores.
How To Get from Salad to Sushi in 3 Moves
At DoorDash, we want to make it as easy as possible for people to discover and order from great restaurants in their neighborhoods.