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On the move in Zurich

Things to do and ways to get there

Featured image for How Uber Uses Ray® to Optimize the Rides Business

Engineering, Backend, Data / ML, Uber AI

How Uber Uses Ray® to Optimize the Rides Business

Large-scale computation is a major back end and infrastructure challenge for Uber to solve as we scale. We applied a compute engine called Ray® in Uber’s marketplace to improve computation efficiency and engineering productivity.

9 January / Global

How Uber Eats fuels the University of Miami Hurricanes off the field

6 January / Global

Case study: how the University of Kentucky transformed Wildcab with Uber

2 January / Global

Moving STRIPES: innovating student transportation at Mizzou

30 December 2024 / Global

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Your guide to NJ TRANSIT’s Access Link Riders’ Choice Pilot 2.0

13 December 2024 / Global

Engineering, Data / ML

Serving Millions of Apache Pinot™ Queries with Neutrino

11 December 2024 / Global

Engineering, Data / ML, Uber AI

Introducing the Prompt Engineering Toolkit

26 November 2024 / Global

Engineering, Backend

The Accounter: Scaling Operational Throughput on Uber’s Stateful Platform

21 November 2024 / Global

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Enabling Infinite Retention for Upsert Tables in Apache Pinot

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Unified Checkout: Streamlining Uber’s Payment Ecosystem

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Open Source and In-House: How Uber Optimizes LLM Training

17 October 2024 / Global

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Genie: Uber’s Gen AI On-Call Copilot

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Making Uber’s ExperimentEvaluation Engine 100x Faster

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