Code Migration in Production: Rewriting the Sharding Layer of Uber’s Schemaless Datastore

Migrating our Schemaless sharding layer from Python to Go while in production demonstrated that it was possible for us to rewrite the frontend of a massive datastore with zero downtime.

Introducing the Uber AI Residency

Interested in accelerating your career by tackling some of Uber’s most challenging AI problems? Apply for the Uber AI Residency, a research fellowship dedicated to fostering the next generation of AI talent.

Building Reliable Reprocessing and Dead Letter Queues with Apache Kafka

The Uber Insurance Engineering team extended Kafka’s role in our existing event-driven architecture by using non-blocking request reprocessing and dead letter queues (DLQ) to achieve decoupled, observable error-handling without disrupting real-time traffic.

Implementing Model-Agnosticism in Uber’s Real-Time Anomaly Detection Platform

Uber Engineering extended our anomaly detection platform's ability to integrate new forecast models, allowing this critical on-call service to scale to meet more complex use cases.

Designing Uber’s Product Manager Bootcamp

Uber’s Product Manager Bootcamp facilitates a more robust and streamlined onboarding experience for new PMs, leading to increased alignment, communication, and collaboration between product teams.

Meet Uber’s Software Engineer Apprentices

Uber's Software Engineer Apprentice Program gives developers with non-traditional paths to programming an opportunity to work on industry-level software while receiving extended training and mentorship.

NEAL, Uber’s Open Source Language-Agnostic Linting Platform

Not Exactly a Linter (NEAL) takes code reviews one step closer to full automation by allowing engineers to write custom syntax-based rules in any language.

Omphalos, Uber’s Parallel and Language-Extensible Time Series Backtesting Tool

Uber Engineering created Omphalos, our new backtesting framework, to enable efficient and reliable comparison of forecasting models across languages.

Playing the Perfect Game: Building Uber Eats on Android

To mark the two-year anniversary of Uber Eats, Android engineer Hilary Karls discusses how her team's commitment to "playing the perfect game" resulted in one of Uber’s most successful products.

SBNet: Leveraging Activation Block Sparsity for Speeding up Convolutional Neural Networks

Uber ATG Toronto developed Sparse Blocks Network (SBNet), an open source algorithm for TensorFlow, to speed up inference of our 3D vehicle detection systems while lowering computational costs.

Harnessing Code Generation to Increase Reliability & Productivity on iOS at Uber

Uber's mobile engineers leverage code generation to make our applications more reliable and boost developer productivity.

Engineering Confidence: A Beginner’s Guide to Overcoming Imposter Syndrome

How do you overcome imposter syndrome? Act with confidence, follow your first instinct, and always be learning and teaching.

COTA: Improving Uber Customer Care with NLP & Machine Learning

In this article, Uber Engineering introduces our Customer Obsession Ticket Assistant (COTA), a new tool that puts machine learning and natural language processing models in the service of customer care to help agents deliver improved support experiences.

Architects of Infrastructure: Meet Uber Aarhus Engineering

Get to know Uber Aarhus Engineering and the work they do behind the scenes to build and maintain our storage and compute platforms.

Year in Review: 2017 Highlights from Uber Open Source

As we approach the New Year, Uber Open Source revisits some of Uber Engineering's most popular projects from 2017.

Year in Review: 2017 Highlights from the Uber Engineering Blog

To ring in the New Year, the Uber Engineering Blog shares some of our editor's picks for 2017.

Welcoming the Era of Deep Neuroevolution

By leveraging neuroevolution to train deep neural networks, Uber AI Labs is developing solutions to solve reinforcement learning problems.

Unifying Mobile Onboarding Experiences at Uber

By unifying mobile onboarding experiences for our new rider app, Uber Engineering made it easier than ever before for users to "get moving."

Navigating the Engineering Interview Process at Uber & Beyond

Up for the challenge of developing at unprecedented scale? First, learn what it takes to master the technical interview process at Uber.

Gleaning Insights from Uber’s Partner Activity Matrix with Genomic Biclustering and Machine Learning

Uber Engineering's partner activity matrix leverages biclustering and machine learning to better understand the diversity of user experiences on our driver app.

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