DoorDash has rich image data collected by Dashers, our delivery drivers, that we use in a number of use cases.
Category Archives: engineering
Improving Subgroup Analysis with Stein Shrinkage
DoorDash is often interested in knowing not only the average effect of an intervention, but also the more granular effect of that intervention on specific cohorts.
Pioneering DoorDash’s Platform Evolution in Pittsburgh
Today, I’m excited to announce that DoorDash is building an engineering team in the “Steel City” – Pittsburgh, PA.
Building Frictionless MFA to Protect Against Account Takeovers
With the rise of digital accounts that enable impactful transactions, keeping these accounts secure from unauthorized account takeovers is becoming essential for any online business. With millions of regular users and the ability to spend money or order food, keeping accounts secure is a top priority at DoorDash as well.
DoorDash 2021 Summer Intern Projects
DoorDash prides itself on offering an internship experience where interns fully integrate with Engineering teams and get the kind of real industry experience that is not taught in a classroom.
Eight Things We Learned from Implementing Payments in the DoorDash Android App
Effective implementation of payments in a mobile app requires precise attention to factors such as payment methods, the user experience, and fraud prevention.
How to Run Apache Airflow on Kubernetes at Scale
As an orchestration engine, Apache Airflow let us quickly build pipelines in our data infrastructure.
The 4 Principles DoorDash Used to Increase Its Logistics Experiment Capacity by 1000%
In our real-time delivery logistics system, the environment, behavior of Dashers (our term for delivery drivers), and consumer demand are highly volatile.
Separating User Data with Multi-tenancy To Improve User Management
As DoorDash expanded and grew its product lines, we needed to find a better way to manage user data.
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.