Joe Zhou, the 7th iOS engineer on the Uber Eats team, offers advice for those considering taking the plunge into programming.
Marmaray: An Open Source Generic Data Ingestion and Dispersal Framework and Library for Apache Hadoop
Today we introduce Marmaray, an open source framework allowing data ingestion and dispersal for Apache Hadoop, realizing our vision of any-sync-to-any-source functionality, including data format validation.
In an interview for the Uber Eng blog, Data Scientist Sunny Jeon talks about how his team develops solutions in order to advance Uber's core value of safety.
Uber Eats engineers describe how they surface restaurant recommendations in the app using multi-objective optimization to give eaters the most satisfying experience while maintaining the health of the Uber Eats marketplace.
Uber built the next generation of COTA by leveraging deep learning models, thereby scaling the system to provide more accurate customer support ticket predictions.
Afshine and Shervine Amidi, identical twins, discuss their journeys to data science and how their work at Uber helps teams improve user experiences on our platform.
Uber developed Maze, our funnel visualization platform, to identify possible UX bottlenecks and provide insight into the various ways riders and drivers interact with our platform.
A few of Uber's over 200 engineering interns from this year's summer program talk about the projects they worked on and what their experiences in the office were like.
M3, Uber's open source metrics platform for Prometheus, facilitates scalable and configurable multi-tenant storage for large-scale metrics.
Databook, Uber's in-house platform for surfacing and exploring contextual metadata, makes dataset discovery and exploration easier for teams across the company.
Fusion.js, Uber's new open source web framework, supports modern features and integrations that make it easy to build lightweight, high-performing apps for the web.
Facing the need for a resilient data structure over thousands of storage nodes to serve the 15 million rides per day that occur on our platform, Uber engineers developed Herb, our data replication solution. Herb ensures data availability and integrity across our data centers.
Building robust regression analysis not only helps ensure that new feature deployments work properly, but also lets Uber engineers automate deployments.
Maps make up the bedrock of Uber's transportation solutions. Find out how we ensure the quality of our map data through extensive metrics computation, maintaining fidelity to real world locations and pinpointing allowable pick up and drop off locations for riders.
As powerful and widespread as convolutional neural networks are in deep learning, AI Labs’ latest research reveals both an underappreciated failing and a simple fix.
Uber developed its own financial planning software, relying on data science and machine learning, to deliver on-demand forecasting and optimize strategic and operations decisions.
Uber open sourced JVM Profiler, our distributed profiler, to enable others to seamlessly collect JVM performance and resource usage metrics.
With a solid margin, Uber senior data scientist Slawek Smyl won the M4 Competition with his hybrid Exponential Smoothing-Recurrent Neural Networks (ES-RNN) forecasting method.
Uber's attorneys explain the intricacies of different types of open source software licenses and intellectual property.
Using GPS and sensor data from Android phones, Uber engineers develop a state model for trips taken by Uber Eats delivery-partners, helping to optimize trip timing for delivery-partners and eaters alike.
To detect and prevent fraud, Uber brings to bear data science and machine learning, analyzing GPS traces and usage patterns to identify suspicious behavior.
Established in 2014 as one of Uber's first distributed engineering sites, Uber Sofia is home to our Tax & Compliance Engineering team, a group responsible for developing the technologies that power our key reporting and compliance services.
Uber engineers share how we process search terms for our Uber Eats service, using query understanding and expansion to find restaurants and menu items that best match what our eaters want.
On April 19, 2018, Uber's LadyEng group hosted Going Global: Uber Tech Day, our second annual event focused on showcasing the technical work of engineers, data scientists, and product managers from across the company.
Making JUMP Bikes' semi-dockless electric bicycles available on Uber's platform not only added a popular new transportation type for Uber riders, but also marked an important step in how we can use our technology to broaden transportation options.
From Beautiful Maps to Actionable Insights: Introducing kepler.gl, Uber’s Open Source Geospatial Toolbox
Created by Uber's Visualization team, kepler.gl is an open source data agnostic, high-performance web-based application for large-scale geospatial visualizations.
Shan He, the technical lead on Uber's kepler.gl framework, discusses her journey to data visualization and why she believes open source is such an important part of her team's work.
deck.gl v5 incorporates simplified APIs, scripting support, and framework agnosticism, making the popular open source data visualization software more accessible than ever before.
Uber’s Observability Applications team overhauled our anomaly detection platform’s workflow to enable the intuitive and performant backfilling of forecasts, paving the way for more intelligent alerting.
Customer-focused Engineering at Uber: A Q&A with Jörg Heilig, VP of Ridesharing and Eats Engineering
In this interview, Uber Vice President of Engineering for Ridesharing and Eats Jörg Heilig talks about taking a leadership role in a large engineering organization with a broad portfolio and the priorities being set for 2018.
uRate empowers both Uber employees and customers to provide quick and efficient feedback on tools and products, enabling engineers to build more responsive services.
Product Manager Zach Singleton talks about how Uber partnered with The Hidden Genius Project to create the Career Prep Program, a one-year course that prepares black male computer science students for careers in tech.
Uber's Mobile Engineering team open sources Nanoscope, a new method tracing tool for Android that enables developers to more accurately debug difficult performance issues.
Curious about what it is like to traverse the high-dimensional loss landscapes of modern neural networks? Check out Uber AI Labs’ latest research on measuring intrinsic dimension to find out.
Applying hardware acceleration to deep neuroevolution in what is now an open source project, Uber AI Labs was able to train a neural network to play Atari in just a few hours on a single personal computer, making this type of research accessible to a far greater number of people.
Uber’s Sensing, Inference, and Research team released a software upgrade for GPS on Android phones that significantly improves location accuracy in urban environments.
Uber Labs leverages mediation modeling to better understand the relationship between product updates and their outcomes, leading to improved customer experiences on our platform.
Matthew Mengerink, Vice President of Engineering for Uber’s Core Infrastructure group, talks about how converging technologies and cloud computing contribute to stable and scalable growth.
Differentiable Plasticity is a new machine learning method for training neural networks to change their connection weights adaptively even after training is completed, allowing a form of learning inspired by the lifelong plasticity of biological brains.
Uber's Data Infrastructure team overhauled our approach to scaling our storage infrastructure by incorporating several new features and functionalities, including ViewFs, NameNode garbage collection tuning, and an HDFS load management service.
Fighting Resistance, Finding Balance: A Conversation with Sophia Vicent, Uber’s Director of Technical Program Management
Sophia Vicent joined Uber after spending 10 years away from the workforce to raise her daughter. We caught up with her to discuss her journey in technical program management.
Uber's Customer Obsession Engineering team developed new check-in queuing and appointment systems to improve the customer experience for driver-partners at our Greenlight Hubs.
Nicolas Garcia Belmonte, head of visualization, talks about his experience getting started in open source and the role it plays in his work at Uber.
Uber Engineering built QALM, a smart load management tool allowing for graceful degradation by preserving critical system requests and shedding non-critical requests.
Uber AI Labs introduces Visual Inspector for Neuroevolution (VINE), an open source interactive data visualization tool to help neuroevolution researchers better understand this family of algorithms.