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.
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.
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.
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.
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.
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.
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.
Uber Engineering created Omphalos, our new backtesting framework, to enable efficient and reliable comparison of forecasting models across languages.
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.
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.
Uber's mobile engineers leverage code generation to make our applications more reliable and boost developer productivity.
How do you overcome imposter syndrome? Act with confidence, follow your first instinct, and always be learning and teaching.
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.
Get to know Uber Aarhus Engineering and the work they do behind the scenes to build and maintain our storage and compute platforms.
As we approach the New Year, Uber Open Source revisits some of Uber Engineering's most popular projects from 2017.
To ring in the New Year, the Uber Engineering Blog shares some of our editor's picks for 2017.
By leveraging neuroevolution to train deep neural networks, Uber AI Labs is developing solutions to solve reinforcement learning problems.
By unifying mobile onboarding experiences for our new rider app, Uber Engineering made it easier than ever before for users to "get moving."
Up for the challenge of developing at unprecedented scale? First, learn what it takes to master the technical interview process at Uber.
Uber Engineering's partner activity matrix leverages biclustering and machine learning to better understand the diversity of user experiences on our driver app.
Uber Engineering built Denial by DNS, our open source solution for preventing DoS by DNS outages, to facilitate more reliable experiences on Uber's apps, no matter how users choose to access them.
How does Uber keep New Year's Eve and other high traffic events...well, uneventful? By keeping our networks extensible and our services reliable year-round.
Arriving now: Uber's Chief Scientist Zoubin Ghahramani introduces Uber AI Labs' newest team member, award-winning neuroscientist Peter Dayan.
In this article, we highlight how Uber leverages machine learning and artificial intelligence to tackle engineering challenges at scale.
Pyro is an open source probabilistic programming language that unites modern deep learning with Bayesian modeling for a tool-first approach to AI.
Uber Engineering's On-Call Dashboard provides real-time incident response, shift maintenance, and post-mortem analysis for an improved on-call experience.
Uber Engineering's data science workbench (DSW) is an all-in-one toolbox that leverages aggregate data for interactive analytics and machine learning.
In this article, members of Uber Bangalore Engineering discuss their role in building reliable transportation systems at scale for India—and beyond.
Uber Engineering built and open sourced NullAway, our fast and practical tool for eliminating NPEs, to help others deploy more reliable Android apps.