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Building Uber’s Go Monorepo with Bazel

In traditional industries such as automobile or aerospace, engineers first design the products and the manufacturing facilities produce the cars or aircrafts according to the design. In software development, a build system is similar to the manufacturing facilities that take

The Journey To Android Monorepo: The History Of Uber Engineering’s Android Codebase Organization

During our inaugural Uber Technology Day, software engineer Aimee Lucido delivered a presentation on the history of Uber Engineering’s Android codebase. In this article, she expands on the reasons behind Uber’s decision to build a monorepo to support

Faster Together: Uber Engineering’s iOS Monorepo

Over the past few years, Uber has experienced a period of hypergrowth, expanding to service over 550 cities worldwide. To keep up, our mobile team also had to grow. In 2014, we had just over a dozen mobile engineers working

Handling Flaky Unit Tests in Java

Introduction to Flaky Tests

Unit testing forms the bedrock of any Continuous Integration (CI) system. It warns software engineers of bugs in newly-implemented code and regressions in existing code, before it is merged. This ensures increased software reliability. It also

Designing Edge Gateway, Uber’s API Lifecycle Management Platform

The making of Edge Gateway, the highly-available and scalable self-serve gateway to configure, manage, and monitor APIs of every business domain at Uber.

Evolution of Uber’s API gateway

In October 2014, Uber had started its journey of scale in what

Introducing Piranha: An Open Source Tool to Automatically Delete Stale Code

At Uber, we use feature flags to customize our mobile app execution, serving different features to different sets of users. These flags allow us to, for example, localize the user’s experience in different regions where we operate and, more importantly,

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Uber ATG self-driving cars

Developing the Next Generation of Coders: Announcing the Dev/Mission Uber Coding Fellowship

Building Uber’s Go Monorepo with BazelAnnouncing a New Framework for Designing Optimal Experiments with PyroMonitoring Data Quality at Scale with Statistical Modeling Enhanced POET: Open-Ended Reinforcement Learning through Unbounded Invention of Learning Challenges and their Solutions

Introducing Piranha: An Open Source Tool to Automatically Delete Stale CodeSessionizing Uber Trips in Real TimeSBNet: Leveraging Activation Block Sparsity for Speeding up Convolutional Neural NetworksFostering a Culture of Sponsorship: Introducing Uber’s Engineering and Sponsorship Development ProgramFostering a Culture of Sponsorship: Introducing Uber’s Engineering and Sponsorship Development Program Why We Leverage Multi-tenancy in Uber’s Microservice ArchitectureScaling Mobile Device Management for macOS with Chef at UberCelebrating International Women’s Day: Meet the Women Building Uber’s Global Platform

The Uber Boulder Tech Site had the pleasure of hosting Claudia Folska Ph.D. for an engaging talk about her work on Wayfinding Without Vision and Neuroplasticity to Cognitive Efficiency Models. The discussion ranged from how the Uber app functions for visually impaired customers to the similarities between how visually impaired people and autonomous vehicles interact with the built world. Claudia, who is visually impaired, serves on the board of directors of the Colorado Regional Transportation District and is an entrepreneur in paratransit. She is also the author of a book “Making ¢ of Pedestrian Oriented Developments-Feet on the Street” in which she examines and rethinks current auto-centered design paradigms for pedestrian-oriented developments. #AutonomousVehicles #MachineVision #ParatransitAdvanced Technologies for Detecting and Preventing Fraud at UberWomen in Data Science at Uber: Moving the World With Data in 2020—and BeyondNo Coding Required: Training Models with Ludwig, Uber’s Open Source Deep Learning ToolboxAlex Popov at SHEleader@digitalIntroducing Fusion.js: A Plugin-based Universal Web FrameworkIntroducing Uber’s Open Source PrinciplesUnder the Hood of Uber ATG’s Machine Learning Infrastructure and Versioning Control Platform for Self-Driving VehiclesPeloton: Uber’s Unified Resource Scheduler for Diverse Cluster WorkloadsEvolving Michelangelo Model Representation for Flexibility at Scale

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Solving Big Data Challenges with Data Science at Uber

Profiles in Coding: Rick Boone, Core Infrastructure, San Francisco

Tricks of the Trade: Tuning JVM Memory for Large-scale Services

Engineering Uber’s Self-Driving Car Visualization Platform for the Web

Thank You for Your Feedback: Improving the Uber Engineering Workflow with uRate

Engineering Uncertainty Estimation in Neural Networks for Time Series Prediction at Uber

Consistent Data Partitioning through Global Indexing for Large Apache Hadoop Tables at Uber

Architecting a Safe, Scalable, and Server-Driven Platform for Driver Preferences with RIBs

Food Discovery with Uber Eats: Using Graph Learning to Power Recommendations

Uber Eng Sofia Meetup: Building Reliable Distributed Systems

Building the Future of Mobility from the Pacific Northwest: Meet the Uber Seattle Tech Team

DBEvents: A Standardized Framework for Efficiently Ingesting Data into Uber’s Apache Hadoop Data Lake

Searchable Ground Truth: Querying Uncommon Scenarios in Self-Driving Car Development

Peloton: Uber’s Unified Resource Scheduler for Diverse Cluster Workloads

Building a Backtesting Service to Measure Model Performance at Uber-scale

Science at Uber: Powering Uber’s Ridesharing Technologies Through Mapping

Counting Calories: How We Improved the Performance and Developer Experience of UberEats.com

Women in Data Science at Uber: Moving the World With Data in 2020—and Beyond

Designing a Production-Ready Kappa Architecture for Timely Data Stream Processing

Engineering SQL Support on Apache Pinot at Uber

Open Sourcing Manifold, a Visual Debugging Tool for Machine Learning

Uber Visualization Highlights: Displaying City Street Speed Clusters with SpeedsUp

Building the Future of Mobility from the Pacific Northwest: Meet the Uber Seattle Tech Team

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Uber is changing the way cities move globally through our transportation and mobility solutions. Located blocks from the iconic Pike Place Market with views over Puget Sound, the Uber Seattle office, home to nearly 500 employees, furthers our core business

Year in Review: 2019 Highlights from the Uber Engineering Blog

With tech offices around the world, Uber engineers are responsible for building new features and systems that improve rideshare, new mobility, food delivery, and other services enabled by our platform. Our Uber Engineering Blog highlights some of these efforts, giving

Uber Infrastructure in 2019: Improving Reliability, Driving Customer Satisfaction

Every day around the world, millions of trips take place across the Uber network, giving users more reliable transportation through ridesharing, bikes, and scooters, drivers and truckers additional opportunities to earn, employees and employers more convenient business travel, and hungry

Gaining Confidence and Improving Android Developer Workflows as a Software Engineering Intern at Uber

Ankit Agrawal was a Summer 2019 intern on Uber’s Developer Experience team, focused on building developer tools for Android Java engineers. In this article, he offers a bird’s eye view of his internship experience at Uber.

February 12, 2019 was

Using GraphQL to Improve Data Hydration in our Customer Care Platform and Beyond

When a customer contacts Uber with a support issue, we want to quickly and seamlessly address their concerns. 

To make the customer support ticket resolution process as streamlined as possible, our Customer Obsession Engineering team designed and developed a new

Measuring Kotlin Build Performance at Uber

This article was written in collaboration with the Kotlin team at JetBrains.

At Uber, we strive to maintain a modern tech stack in all our applications. A natural progression in the Android space was to start adopting Kotlin,

Optimizing M3: How Uber Halved Our Metrics Ingestion Latency by (Briefly) Forking the Go Compiler

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In Uber’s New York engineering office, our Observability team maintains a robust, scalable metrics and alerting pipeline responsible for detecting, mitigating, and notifying engineers of issues with their services as soon as they occur. Monitoring the health of our thousands

Managing Uber’s Data Workflows at Scale

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At Uber’s scale, thousands of microservices serve millions of rides and deliveries a day, generating more than a hundred petabytes of raw data. Internally, engineering and data teams across the company leverage this data to improve the Uber experience.

How to Ship an App Rewrite Without Risking Your Entire Business

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This article is the fifth in a series covering how Uber’s mobile engineering team developed the newest version of our driver app, codenamed Carbon, a core component of our ridesharing business. Among other new features, the app lets our population

M3: Uber’s Open Source, Large-scale Metrics Platform for Prometheus

To facilitate the growth of Uber’s global operations, we need to be able to quickly store and access billions of metrics on our back-end systems at any given time. As part of our robust and scalable metrics infrastructure, we built

Building Check-In Queuing & Appointment Scheduling for In-Person Support at Uber

With over 700 locations worldwide, Uber’s Greenlight Hubs (GLH) provide in-person support for driver-partners for everything from account and payment issues to vehicle inspections and driver onboarding. To create better experiences for driver-partners and improve customer satisfaction, Uber’s Customer Obsession

Engineering NullAway, Uber’s Open Source Tool for Detecting NullPointerExceptions on Android

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Maintaining the reliability of Uber’s mobile apps is crucial to facilitating a seamless and enjoyable user experience. Alongside a robust plugin architecture, feature flags, and dynamic validation of external data, static analysis tools play a key role

How to Have Your Software Engineering Job and Eat It Too

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I was never sure if I wanted to be a software engineer.

Don’t get me wrong, I loved coding. I loved solving hard puzzles, thinking about products, and working in a collaborative environment. I loved advocating for users, interfacing across