Announcing the 2019 Uber AI Residency

Announcing the 2019 Uber AI Residency

By Theofanis Karaletsos, Ersin Yumer, Raquel Urtasun, and Zoubin Ghahramani on behalf of Uber AI Labs and Uber ATG

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. 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 AI Labs or Uber Advanced Technologies Group (ATG).


Artificial intelligence at Uber

Uber’s AI efforts are clustered around two main areas: general AI and applied machine learning through Uber AI, and AI for self-driving cars through Uber ATG.

Uber AI

The Uber AI organization in San Francisco combines efforts from Uber AI Labs in basic research and applications with various platform teams working towards providing and improving services for the rest of the company in fields such as computer vision, conversational AI, and sensing and inference from sensor data. Uber AI Labs is composed of two main wings that feed and reinforce each other: foundational core research and our Connections group, a team focusing on the translation of research into applications for the company in collaboration with platform and product teams.

AI Labs Core

AI Labs Core works on diverse topics ranging the spectrum from probabilistic programming and Bayesian inference to core deep learning research, reinforcement learning, neuroevolution, safety, and artificial intelligence.

AI Labs Connections

AI Labs is working on applied research which can also advance the mission of Uber today. For example, Connections transformed Bayesian optimization from a research field into a service for the company with applications for multiple teams and has deep collaborations with teams working on natural language processing, conversational AI, forecasting, mapping, fraud detection, Uber’s Marketplace, and many other areas.

Uber ATG

One of the most ambitious AI applications at Uber is self-driving vehicles. In the context of self-driving technology, 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 these groups, 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., NIPS, 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 (also, see Pyro: Deep Universal Probabilistic Programming), a pioneering effort in systems research combining ideas from Bayesian Inference and deep learning.


The Residency program

Uber AI Residents will be chosen across both AI Labs in San Francisco and ATG in Toronto and San Francisco. Residents will have the opportunity to pursue interests across academic and applied research, meeting both with researchers at AI Labs and ATG, as well as Uber product and engineering teams to converge on initial project directions. Residents will also be encouraged to publish their work externally at top machine learning venues (NIPS, ICLR, ICML, CVPR, EMNLP, ACL, ECCV, ICCV etc.) and through Eng Blog articles, or by releasing open source projects.

Pursuing projects that span disciplines and teams is encouraged. For instance, our 2018 residency class is currently working on foundational research projects in probabilistic modeling, deep learning, 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.


Apply today

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, including details about the role, required qualifications, and instructions about submitting academic records and any other required documents.

Applications will be considered as early as December 10th, 2018, and are due by Sunday, January 13, 2019 at 11:59 p.m. EST. Additional candidacy communications will be given on a stage-to-stage basis, and finalists are required to have referrals submit two supporting letters for recommendations by February 18, 2019.


Decisions will be sent by March 15th 2019.


Learn more about the Uber AI Residency program and apply today!

Check out research from Uber AI and ATG.

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Theofanis took his first steps as a machine learner at the Max Planck Institute For Intelligent Systems in collaboration with Microsoft Research Cambridge with work focused on unsupervised knowledge extraction from unstructured data, such as generative modeling of images and phenotyping for biology. He then moved to Memorial Sloan Kettering Cancer Center in New York, where he worked on machine learning in the context of cancer therapeutics. He joined a small AI startup Geometric Intelligence in 2016 and with his colleagues formed the new Uber AI Labs. Theofanis' research interests are focused on rich probabilistic modeling, approximate inference and probabilistic programming. His main passion are structured models, examples of which are spatio-temporal processes, models of image formation, deep probabilistic models and the tools needed to make them work on real data. His past in the life sciences has also made him keenly interested in how to make models interpretable and quantify their uncertainty, non-traditional learning settings such as weakly supervised learning and model criticism.
Ersin Yumer is a Staff Research Scientist, leading the San Francisco research team within Uber ATG R&D. Prior to joining Uber, he led the perception machine learning team at Argo AI, and before that he spent three years at Adobe Research. He completed his PhD studies at Carnegie Mellon University, during which he spent several summers at Google Research as well. His current research interests lie at the intersection of machine learning, 3D computer vision, and graphics. He develops end-to-end learning systems and holistic machine learning applications that bring signals of the visual world together: images, point clouds, videos, 3D shapes and depth scans.
Raquel Urtasun is the Chief Scientist for Uber ATG and the Head of Uber ATG Toronto. She is also a Professor at the University of Toronto, a Canada Research Chair in Machine Learning and Computer Vision and a co-founder of the Vector Institute for AI. She is a recipient of an NSERC EWR Steacie Award, an NVIDIA Pioneers of AI Award, a Ministry of Education and Innovation Early Researcher Award, three Google Faculty Research Awards, an Amazon Faculty Research Award, a Connaught New Researcher Award, a Fallona Family Research Award and two Best Paper Runner up Prize awarded CVPR in 2013 and 2017. She was also named Chatelaine 2018 Woman of the year, and 2018 Toronto’s top influencers by Adweek magazine
Zoubin Ghahramani is Chief Scientist of Uber and a world leader in the field of machine learning, significantly advancing the state-of-the-art in algorithms that can learn from data. He is known in particular for fundamental contributions to probabilistic modeling and Bayesian approaches to machine learning systems and AI. Zoubin also maintains his roles as Professor of Information Engineering at the University of Cambridge and Deputy Director of the Leverhulme Centre for the Future of Intelligence. He was one of the founding directors of the Alan Turing Institute (the UK's national institute for Data Science and AI), and is a Fellow of St John's College Cambridge and of the Royal Society.

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