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Engineering, Data / ML, Uber AI

Reinforcement Learning for Modeling Marketplace Balance

Uber uses reinforcement learning to optimize matching and better balance drivers with demand in real time. By considering long-term outcomes, we improve efficiency and increase driver earnings by proactively aligning drivers with demand.

July 2 / Global

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Engineering, Backend, Uber AI

Slashing CI Costs at Uber

June 23 / Global

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Engineering

The Evolution of Uber’s Search Platform

June 20 / Global

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Engineering, Backend, Security

Automating Kerberos Keytab Rotation at Uber

June 18 / Global

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Engineering, Data / ML

How Uber Migrated from Hive to Spark SQL for ETL Workloads

June 12 / Global

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Data / ML

From Archival to Access: Config-Driven Data Pipelines

June 5 / Global

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Engineering, Data / ML, Uber AI

Enhanced Agentic-RAG: What If Chatbots Could Deliver Near-Human Precision?

May 29 / Global

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Engineering, Backend

Robust Database Backup Recovery at Uber

May 22 / Global

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Engineering, Backend, Security

Building Uber’s Multi-Cloud Secrets Management Platform to Enhance Security

May 15 / Global

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Engineering, Backend, Data / ML

Migrating Large-Scale Interactive Compute Workloads to Kubernetes Without Disruption

May 8 / Global

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Engineering, Backend, Uber AI

Fixrleak: Fixing Java Resource Leaks with GenAI

May 1 / Global

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Engineering, Backend

Migrating Uber’s Compute Platform to Kubernetes: A Technical Journey

April 24 / Global

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Engineering, Data / ML, Uber AI

Advancing Invoice Document Processing at Uber using GenAI

April 17 / Global

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Engineering, Data / ML, Uber AI

Uber’s Journey to Ray on Kubernetes: Resource Management

April 10 / Global

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Transit

Case study: how Wellington County enhances mobility options for rural townships

April 7 / Global

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Engineering, Data / ML, Uber AI

Uber’s Journey to Ray on Kubernetes: Ray Setup

April 3 / Global

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Universities

How medical schools support the next generation of doctors with Uber

April 2 / Global

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