Introducing the Uber AI Residency

Introducing the Uber AI Residency

Uber AI Labs and Uber ATG Toronto are excited to announce the Uber AI Residency, an intensive one-year research training program slated to begin this summer.  

Uber has invested substantially in machine learning and artificial intelligence, with groups around the company working on a variety of techniques—deep learning, reinforcement learning, neuroevolution, probabilistic modeling,  natural language processing, computer vision, and self-driving cars to name just a few—to enable a set of real world applications just as varied. When machine learning works well, Uber can provide improved user experiences across our services. Among other applications, we apply these technologies to developing:

  • Better prediction of spatiotemporal rider demand patterns across a city enables both shorter wait times for riders and shorter gaps between trips for driver-partners, leading to higher average hourly earnings.
  • Models enabling more intelligent fusion of mapping, traffic, and GPS data facilitate more efficient route selection and more precise pick-up and drop-off locations, leading to smoother, more enjoyable trips.
  • Natural language understanding, permitting faster perception and resolution of customer support tickets, for example, enabling a rider to recover the backpack that they left behind on a trip more quickly.
  • Perception, prediction, motion planning, control, localization, and mapping for self-driving vehicles.

At Uber’s scale, these advances or those in directions yet to be explored have the potential to positively impact millions of people.

Machine learning has grown by leaps and bounds over the last decade, vaulting from academic curio to a real-world workhorse powering a vast number of business applications. However, there remains much important (and fun!) science and engineering to be done. Taking inspiration from similar programs, Uber AI Labs and Uber ATG Toronto created the Uber AI Residency to allow up-and-coming researchers accelerate their careers in machine learning and AI research and practice.

Residents will have the flexibility to pursue a range of different directions in research and application with both Uber AI Labs and Uber ATG Toronto. Some projects might involve fundamental AI research, pushing the frontiers of the field by developing new algorithms for learning and control, while others might include devising and training new models to help more efficiently transport people and things in the physical domain, improving real world user experiences. Still other projects might use new or existing models in concert with anonymized data sets to enable understanding of societal conditions, for example, by looking at the wage gap anchored by data. To effectively accomplish research goals drawn from this diverse set of possibilities, residents will have the opportunity to work directly with researchers and engineers at Uber AI Labs, Uber ATG Toronto, and across the entire company.

The Uber AI Residency will last for 12 months, kicking off with an initial period of learning, exploration, and collaborative ideation. During this period, residents will meet with researchers at AI Labs and Uber ATG Toronto, as well as product and engineering teams to converge on initial project directions. Residents will be paired with mentors from Uber AI Labs or Uber ATG Toronto, as well as relevant research teams, to support them throughout their fellowship.  Pursuing projects that span disciplines and teams is encouraged. Residents will be also be encouraged to publish their work externally at top machine learning venues (NIPS, ICLR, ICML, CVPR, EMNLP, ACL, ECCV, ICCV etc.), via blog posts, or by publishing open source projects.

Residents will be embedded directly with Uber AI Labs at our San Francisco headquarters and/or Uber ATG at our Toronto office, and candidates of all academic and geographic backgrounds are encouraged to apply. Applicants who require a work visa will be assessed on a case-by-case basis for those who are accepted.

Sound like fun? Apply by March 18, 2018.

Editor’s Note March 1, 2018: We have updated this article to reflect the inclusion of Uber ATG Toronto as a co-sponsor of this residency. — Molly Vorwerck

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Jason Yosinski
Jason Yosinski is a founding member of Uber AI Labs and there leads the Deep Collective research group. He is known for contributions to understanding neural network modeling, representations, and training. Prior to Uber, Jason worked on robotics at Caltech, co-founded two web companies, and started a robotics program in Los Angeles middle schools that now serves over 500 students. He completed his PhD working at the Cornell Creative Machines Lab, University of Montreal, JPL, and Google DeepMind. He is a recipient of the NASA Space Technology Research Fellowship, has co-authored over 50 papers and patents, and was VP of ML at Geometric Intelligence, which Uber acquired. His work has been profiled by NPR, the BBC, Wired, The Economist, Science, and the NY Times. In his free time, Jason enjoys cooking, reading, paragliding, and pretending he's an artist.
Zoubin Ghahramani
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.
Raquel Urtasun
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