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.General Web
Lessons for developing a fast, flexible, and scalable map feature on webData Machine Learning
DoorDash engineers built Curie, a new experimentation analysis platform, to better gauge the success of product experiments.Backend
Learn how we utilized a custom Kafka solution to reduce outages and enable horizontal scalability for task processingMachine Learning
Learn how we built a classification model quickly, cheaply, and at scaleGeneral
From a product engineering perspective, external partnerships can be tricky. Here are four best practices to follow.Mobile
Learn how a Fluent design pattern can help create easy to read, scaleable, automated UI tests for Android developmentBackend
Reengineering our event-driven delivery service for DoorDash Drive into Kotlin, we added the open source Cadence as a fallback for retries.General
Learn how we built a discrete event simulator for location data testsMobile
Learn how we we were able to utilize the Android Navigation library without sacrificing user experienceMachine Learning
Learn how we utilized cost curves to automate our marketing campaigns at scaleBackend General
Ensure your growing team can search, analyze, and visualize data securely by integrating Splunk with a custom built Terraform provider.