One of the core mottoes on the engineering team at DoorDash is:
We are only as good as our next delivery!
One of the core mottoes on the engineering team at DoorDash is:
We are only as good as our next delivery!
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.
By Rohan Shanbhag and Wei Lin, Software Engineers
Most Android apps rely on network calls to a set of backend services.
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.
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.
One of our goals at DoorDash is to surface to consumers a wide range of stores that are quickly deliverable to their given address.
At DoorDash we recently migrated the codebase of our iOS Consumer and Dasher apps to Swift 3 from Swift 2.
Customers across North America come to DoorDash to discover and order from a vast selection of their favorite stores.
When an engineer at DoorDash opens a GitHub pull request, our goal is to quickly and automatically provide information about code health.
At DoorDash, mobile is an integral part of our end user experience.