Learn about high impact projects that power our velocity, reliability, and innovation.
When the COVID-19 pandemic significantly changed how people took their meal, DoorDash had to retrain demand prediction machine learning models.
Lessons for developing a fast, flexible, and scalable map feature on web
DoorDash engineers built Curie, a new experimentation analysis platform, to better gauge the success of product experiments.
Learn how we utilized a custom Kafka solution to reduce outages and enable horizontal scalability for task processing
Learn how we built a classification model quickly, cheaply, and at scale
From a product engineering perspective, external partnerships can be tricky. Here are four best practices to follow.
Learn how a Fluent design pattern can help create easy to read, scaleable, automated UI tests for Android development
Reengineering our event-driven delivery service for DoorDash Drive into Kotlin, we added the open source Cadence as a fallback for retries.
Learn how we built a discrete event simulator for location data tests
Learn how we we were able to utilize the Android Navigation library without sacrificing user experience
Learn how we utilized cost curves to automate our marketing campaigns at scale
Ensure your growing team can search, analyze, and visualize data securely by integrating Splunk with a custom built Terraform provider.