Uber Engineering built AthenaX, our open source streaming analytics platform, to bring large-scale event stream processing to everyone.
Uber ATG's Poornima Kaniarasu shares how she found her "place" developing the machine learning technologies behind our self-driving vehicles.
The UberEATS Restaurant Manager gives restaurant partners insight into their business by measuring customer satisfaction, sales, and service quality.
What did you do this summer? In this article, intern Mitali Palekar reflects on her experience as a member of Uber's Site Reliability Engineering team.
Uber's Roche Janken shares how her background as a dancer influences her approach to privacy engineering.
Uber Engineering introduces a new Bayesian neural network architecture that more accurately forecasts time series predictions and uncertainty estimations.
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’s Data Visualization team uses their deck.gl and Voyager visualization platforms to map rider behavior during the August 21, 2017 solar eclipse.
Uber Engineering's Data Visualization Team and ATG built a new web-based platform that helps engineers and operators better understand information collected during testing of its self-driving vehicles.
Uber Engineering's XP Background Push mitigates bugs safely and efficiently in real time, facilitating more seamless user experiences on our apps.
Uber Engineering's new open source tool, AutoValue: Bundle Extension, decreases the likelihood of encountering bugs by enabling Android engineers to quickly unbundle data into value classes.
In this article, members of Uber’s Mobile Platform team introduce Startup Reason Reporter, our new open source tool for detecting startup reason on iOS.
Uber Engineering shares our best practices for working with plugins, a powerful tool that enables us to build and ship features quickly at scale.
Uber Engineering built a new microservice to power Driver Profiles, an in-app platform that enhances the Uber experience by celebrating drivers.
Learn how Uber Engineering’s Employee Productivity Tools team built uChat, an internal chat solution capable of scaling to meet the needs of our growing global company.
Uber Engineering architected a real-time trip features prediction system using an open source RESTful search engine built with Elasticsearch, Logstash, and Kibana (ELK).
Chameleon, a global CMS for Uber.com, enables regional operations and marketing teams at Uber to build and ship customized, on-brand webpages.
Snap your fingers and presto! How Uber Engineering built a fast, efficient data analytics system with Presto and Parquet.
Uber Engineering built Uber Central's architecture by integrating the Uber for Business platform with a custom front-end design tailored to customer feedback.
In this article, we discuss how Uber Engineering designed m.uber, a lightweight web app that delivers a native app experience for riders on mobile browsers.
In this article, a software engineer on Uber Engineering's Payments Efficiency Team discusses how we optimized our driver payment platform for cash and digital wallet commissions in India.
Get to know Uber's Developer Experience team, a group of writers, educators, and technologists dedicated to setting our engineers up for success.
Recurrent neural networks equip Uber Engineering's new forecasting model to more accurately predict rider demand during extreme events.
In 2016, Uber Engineering built and open sourced RAVE, a data model validation framework for Android that leverages Java annotation processing to protect against crashes caused by invalid data.
In this article, Uber Engineering shares our best practices for relieving RxJava backpressure on Android through targeted operators, more forgiving RxJava 1.x configurations, and RxJava 2.x.
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.
The monorepo codebase powering Uber Engineering's Android rider app is architected to scale for growth while supporting the IDE, reducing build times, and stabilizing the master during integrations.
In this article, we discuss how Uber Engineering uses Locality Sensitive Hashing on Apache Spark to reliably detect fraudulent trips at scale.