Connecting the digital and physical worlds safely and reliably on the Uber platform presents exciting technological challenges and opportunities. For Uber, artificial intelligence (AI) is essential to developing systems that are capable of optimized, automated decision making at scale.
AI at Uber is a rapidly growing area across both research and applications, including self-driving vehicles. On behalf of Uber, we invite you to join us on our journey as an Uber AI Resident.
Established in 2018, the Uber AI Residency is a 12-month training program for recent college and master’s graduates, professionals who are looking to reinforce their AI skills, and those with quantitative skills and interest in becoming an AI researcher at Uber.
Uber’s AI efforts are clustered around two main areas: general AI and applied machine learning to solve Uber’s challenging problems in ride sharing, Uber Eats delivery, and self-driving vehicles, among others. This year’s AI residency program will focus on our self-driving cars project through Uber Advanced Technology Group (ATG).
One of the most ambitious AI applications at Uber is self-driving vehicles. In the context of self-driving, AI enables our systems to perceive the surrounding environment using multiple sensors, predict the motion and intent of actors in the near future, and plan safe maneuvers for the self-driving vehicle. Creating high definition maps and localizing self-driving vehicles with the precision of a few centimeters are also important components of these technologies that provide critical data about the vehicle’s environment.
Furthermore, learning what and how to simulate is a focus of interest for our researchers at ATG. As all the modules mentioned above are powered by AI, topics related to generative models, reinforcement learning, imitation learning, deep structured models, network architectural search, model compression, learning in the presence of noisy and unstructured data, and other exciting research areas are very actively pursued by our team.
ATG R&D Labs
Uber ATG R&D Labs, spanning our Toronto and San Francisco offices, are potential locations for the Uber AI Residency program, providing a unique opportunity to work with distinguished researchers to develop advanced machine learning and computer vision techniques for solving one of the most challenging problems of the century.
Open source & publication opportunities
Across Uber, we are committed to an open and inclusive research mission that benefits the community at large through both Uber AI and Uber ATG Research. We actively publish papers across our interest domains in top conferences (e.g., NeurIPS, ICLR, ICML, CVPR, EMNLP, ACL, ECCV, ICCV, IROS, ICRA, CoRL). We are also active in giving back to the machine learning community through high profile open source projects such as the Pyro probabilistic programming language, a pioneering effort in systems research combining ideas from Bayesian Inference and deep learning, and Ludwig, a code-free deep learning toolbox.
The Residency program
Uber AI Residents will be chosen across San Francisco and Toronto locations. Residents will have the opportunity to pursue interests across academic and applied research, meeting both with researchers, as well as Uber product and engineering teams to converge on initial project directions. Residents are often able to share their work with the broader community through public presentations, blog posts, or open source releases.
Pursuing projects that span disciplines and teams is encouraged. For instance, our 2019 residency class is currently working on foundational research projects in probabilistic modeling, deep learning, and reinforcement learning, as well as computer vision. They have multiple results submitted to top scientific venues, and their contributions also directly impact Uber’s business in partnership with Uber’s technology teams.
Applications are open now! We encourage applying well in advance, as applications are evaluated on a rolling basis.
Applicants can find additional information about the residency on our website. Details about the role, required qualifications, and instructions about submitting academic records and any other required documents can be found on the application page.
Applications will be considered as early as January 6, 2020, and are due by Sunday, January 19, 2020 at 11:59 p.m. EST. Additional candidacy communications will be given on a stage-to-stage basis. Finalists who make it to the final rounds of interviews are required to have referrals submit a letter of recommendation by February 21, 2020 at 11:59 p.m. EST.
Decisions will be shared with applications in mid-March, 2020.