Learn about high impact projects that power our velocity, reliability, and innovation.
Learn how we overcame the technical challenges of implementing chat quickly and efficiently for all our Dasher and consumer applications.Backend Web
Migrating DoorDash's business-critical session management system in a disruption-free manner required careful planning and monitoring.Machine Learning
Machine learning model drift occurs as data changes, but a robust monitoring system helps maintain integrity.Culture
Learn how our three pillar approach to hackathons results in successful eventsGeneral
DoorDash announces the opening of its newest and first international engineering office in Toronto.Machine Learning
Learn how we managed to better predict long tail delivery estimations using historical and realtime features as well as custom loss functionsWeb
Learn how DoorDash created the Class Pattern when building the Item Modal in its web application to increase reliability.Backend General
New service releases deployed into DoorDash’s microservice architecture immediately begin processing and serving their entire volume of production traffic. If one of those services is buggy, however, customers may have a degraded experience or the site may go down completely. Although we currently have a traffic management solution under development for gradual service rollouts as ...Culture General
Follow these helpful tips when preparing for DoorDash's technical interview.Backend General
When companies move to microservices, they need to address a new challenge of setting up distributed tracing to identify availability or performance issues throughout the platform. While various tools offered on the market or through open-source perform this task, there is often a lack of standardization, making leveraging these tools costly or complicated. As DoorDash ...Backend General
As applications grow in complexity, memory stability is often neglected, causing problems to appear over time. When applications experience consequences of problematic memory implementations, developers may find it difficult to pinpoint the root cause. While there are tools available that automate detecting memory issues, those tools often require re-running the application in special environments, resulting ...General Machine Learning
Migrating functionalities from a legacy system to a new service is a fairly common endeavor, but moving machine learning (ML) models is much more challenging. Successful migrations ensure that the new service works as well or better than its legacy version. But the complexity of ML models makes regressions more likely to happen and harder ...