Engineering manager Tory Schober talks about creating an Uber Freight engineering team in Chicago, and how that team will benefit from being close to its customer base.
At an April 2019 meetup on ML and AI at Uber Seattle, members of our engineering team discussed three different approaches to enhancing our ML ecosystem.
Logan Jeya, Product Manager, explains how Uber's machine learning platform, Michelangelo, makes it easy to deploy models that enable data-driven decision making.
Uber AI Labs proposes Loss Change Allocation (LCA), a new method that provides a rich window into the neural network training process.
We built Cyborg, an open source implementation of VectorDrawable for iOS, to more easily implement designs across our apps.
Uber ATG Web Platform intern Anat Kleiman shares her advice for testing React version 16 components when altering application logic.
Uber Engineering details how GraphQL integrated into our Customer Care platform, making for more targeted queries and reducing server load.
Waleed Kadous, Principal Engineer at Uber, enjoys tackling technical challenges that bridge the digital and physical duality of Uber's problem space.
We redesigned the Uber Freight app with RIBs, our open source plugin architecture, to enable quicker feature rollouts and an improved user experience.
We sat down with Jeff Clune, Senior Research Manager, to talk about his work in AI, journey to Uber, and Presidential Early Career Achievement in Science and Engineering (PECASE) award.
Suzette Puente, Uber Data Science Manager, shares how she applies her graduate work in statistics to forecast traffic patterns and generate better routes.
Data science helps Uber determine which tables in a database should be off-boarded to another source to maximize the efficiency of our data warehouse.
Zoubin Ghahramani, Head of Uber AI, discusses how we use artificial intelligence techniques to make our platform more efficient for users.
At the Uber Open Summit Sofia 2019, we showcased how open source technologies are driving the future of artificial intelligence, site reliability, and other domains.
With zero downtime, Uber's Payments Engineering team embarked on a migration that would allow authorization hold logic to be written once and used across existing and future payments products.
Dawn Woodard, Director of Data Science, considers travel time prediction one of Uber's most interesting mapping problems.
Uber Poet, an open source mock application generator, helped us determine if refactoring the application part of our code into a few large modules would make our overall Swift build times faster.
We redesigned Uber's web-based booking flow for riders who prefer a browser over the app, simplifying pickup options and speeding up interactivity.
During our 2019 Uber European Technology Showcase, technical teams across the company discussed how we build products that drive safe and reliable transportation.