DoorDash operates a large, active on-demand logistics system facilitating food deliveries in over 4,000 cities.
Category Archives: engineering
The Undervalued Skills Candidates Need to Succeed in Data Science Interviews
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 .
Future-proofing: How DoorDash Transitioned from a Code Monolith to a Microservice Architecture
In 2019, DoorDash’s engineering organization initiated a process to completely reengineer the platform on which our delivery logistics business is based.
Minimizing Risk for API Extraction in a Major Migration Project
DoorDash engineering describes its three step process for safely migrating business logic as APIs.
Building a Gigascale ML Feature Store with Redis, Binary Serialization, String Hashing, and Compression
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.
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.
Hello Seattle: DoorDash Expands its Engineering Footprint to the Pacific Northwest
The newest DoorDash engineering office is seeking engineering talent to support its Drive and DashMart business lines.
Hot Swapping Production Tables for Safe Database Backfills
Making changes to data tables can disrupt production systems that need to be operating 24/7. DoorDash engineering explains how to hot swap tables so as not to interfere with production systems.
Building an Image Upload Endpoint in a gRPC and Kotlin Stack
When moving to a Kotlin gRPC framework for backend services, handling image data can be challenging.
Solving for Unobserved Data in a Regression Model Using a Simple Data Adjustment
Making accurate predictions when historical information isn’t fully observable is a central problem in delivery logistics.