Joe Zhou, the 7th iOS engineer on the Uber Eats team, offers advice for those considering taking the plunge into programming.
Uber Eats engineers describe how they surface restaurant recommendations in the app using multi-objective optimization to give eaters the most satisfying experience while maintaining the health of the Uber Eats marketplace.
Uber Engineering's data science workbench (DSW) is an all-in-one toolbox that leverages aggregate data for interactive analytics and machine learning.
Uber Engineering built AthenaX, our open source streaming analytics platform, to bring large-scale event stream processing to everyone.
The UberEATS Restaurant Manager gives restaurant partners insight into their business by measuring customer satisfaction, sales, and service quality.
Uber Engineering introduces Michelangelo, our machine learning-as-a-service system that enables teams to easily build, deploy, and operate ML solutions at scale.
Uber Engineering architected a real-time trip features prediction system using an open source RESTful search engine built with Elasticsearch, Logstash, and Kibana (ELK).
In this article, we outline how Uber Engineering developed UberSignature, a new feature that allows iOS users to draw and store touchscreen signatures on the UberRUSH app.
Get to know Uber Engineering New York City and the exciting work we do on UberEATS, UberRUSH, and Uber's Observability platform.
A recipe for success: how Uber Engineering used React Native to optimize UberEATS' Restaurant Dashboard app for mobile.
In November 2016 Uber unveiled a sleek new rider app. The app implements a new mobile architecture across both iOS and Android. In this article, Uber Engineering discusses why we felt the need to create a new architecture pattern, and how it helps us reach our goals.
The end of a two-part series on the tech stack that Uber Engineering uses to make transportation as reliable as running water, everywhere, for everyone, as of spring 2016.