We’re changing the way people think about transportation. Not that long ago we were just an app to request premium black cars in a few metropolitan areas. Now we’re a part of the logistical fabric of more than 600 cities around the world. Whether it’s a ride, a sandwich, or a package, we use technology to give people what they want, when they want it.
For the people who drive with Uber, our app represents a flexible new way to earn money. For cities, we help strengthen local economies, improve access to transportation, and make streets safer.
And that’s just what we’re doing today. We’re thinking about the future, too. With teams working on new modalities, self-driving cars and even urban air transportation, we’re in for the long haul. We’re reimagining how people and things move from one place to the next.And that’s just what we’re doing today. We’re thinking about the future, too. With teams working on new modalities, self-driving cars and even urban air transportation, we’re in for the long haul. We’re reimagining how people and things move from one place to the next.
About the Role:
Uber is looking for adaptable computer vision/machine learning experts who are not afraid to get their hands dirty with real world issues to work on impactful problems. We are looking for folks who have a good balance of theoretical knowledge and programming expertise. You will be rubbing shoulders with world class scientists and engineers, looking to revolutionize transportation. You will get a chance to use your skills to solve some of the toughest problems in the geo-spatial domain using state-of-the-art compute infrastructure and tools.
Do you live on the edge between research and engineering, especially in areas like machine learning and computer vision? Do you like not just inventing new algorithms, but seeing them all the way into devices that move through the real world?
We’re looking for people who have the following characteristics:
- Fast learner. We’re looking for team members who thrive on applying their knowledge, learning new technologies and don’t believe in one-size-fits-all solutions. You should be able to adapt easily to meet the fast pace of a rapidly evolving research, development, and testing environment.
- Fearlessness. You think a working proof-of-concept is the best way to make a point. You strive on proving that speed and quality are not conflicting; that you can achieve both at the same time.
- Versatility. In addition to having an intimate knowledge of core engineering fields, you understand how all the pieces fit together into integrated systems, and how they impact performance.
- Passion. You feel ownership over everything you ship; you'd never call code or design "released" until you’re confident it’s correct. You pride yourself on efficient monitoring, strong documentation, and proper test coverage.
- A team player. You believe that you can achieve more on a team — that the whole is greater than the sum of its parts. You rely on others' candid feedback for continuous improvement.
What You’ll Need
- Masters, with a PhD preferred.
- Expertise with computer vision and/or machine learning, especially deep learning.
- Interest in applying machine learning & computer vision to resource constrained devices (such as mobile phones)
- Interest in Android development (with experience preferred)
- Ability to whiteboard some theory while at the same time rolling up your sleeves and get coding.
- Understand research papers and be able to translate the ideas into efficient code quickly.
- Data oriented - i.e. be able to set up experiments to measure things that will in turn drive decisions.
About the Team
As part of Uber’s larger sensing effort, we are building new capabilities that will allow us extract valuable bits of information from imagery and sensors. This will have a direct impact on both riders and drivers by helping them connect faster and get to their destinations more efficiently and safely. The Video Intelligence team is responsible for developing state of the art computer vision and machine learning solutions to analyze real-time image and video data, and deriving inferences that directly drive efficiencies across the company.
Be sure to check out the Uber Engineering Blog to learn more about the team.