Do work that matters
AI that moves the world
Uber AI Labs gathers top scientists and engineers to create the next generation of machine learning algorithms to improve the lives of millions of people worldwide.
Come help us turn the dreams of science fiction into reality. We conduct basic and applied research into problems such as self-driving cars, urban aviation, optimizing cities, and keeping riders safe.
The lab offers a unique set of challenges; vast computational resources; talented, diverse colleagues; the ability to publish, open-source, and otherwise share your research; and data the gods would envy.
Uber AI in the press
Geometric Intelligence takes the driver seat at Uber as Uber AI labs
Wired reports on the efforts at Uber to re-invent itself as an “AI-first” company with products ranging from self-driving trucks to self-flying cars. Geometric Intelligence was acquired to lead this company-wide transformation as the Uber AI labs.
Zoubin Ghahramani: a natural choice for Chief Scientist
The Chief Product Officer Jeff Holden, now part of the Executive Leadership Team at Uber, welcomes Zoubin as the Chief Scientist at Uber, overseeing all AI and ML strategy globally across the company. He explains why Uber is a “natural fit” for this role.
Ken’s life mission: Evolving brains inside machines
Ken Stanley is the world expert in the computational modeling of evolution, the only optimizer known to produce strong AI.
Jeff Clune discusses recent progress in generative models
A Nature News story features Jeff Clune describing the dramatic recent progress in generative models, which increasingly can synthesize photo-realistic images, including his work at Uber AI Labs with Jason Yosinski and Anh Nguyen .
Nature: Zoubin and Jeff on opening the black box of AI
Nature article features Chief Scientist Zoubin Ghahramani, his insights on AI interpretability, and more transparent reasoning with his Automatic Statistician.
Science: Jason’s new role as AI Detective deciphering deep learning
Science magazine interviews Jason Yosinski at Uber HQ on his new role as the “AI detective”, cracking open the black box of deep learning and pushing toward AI Neurosicence as a proper discipline.
Jeff Clune discusses robots that can rapidly adapt if damaged
The BBC interviews Jeff Clune about a Nature cover article he co-authored on using machine learning to enable robots to gracefully adapt to damage in under two minutes.
Meet the team
Zoubin Ghahramani

RESEARCH AREA

  • Probabilistic Machine Learning

Zoubin Ghahramani is Chief Scientist at Uber. He is also a Professor of Information Engineering at the University of Cambridge, and Deputy Academic Director of the Leverhulme Centre for the Future of Intelligence. Until recently he was also the founding Cambridge Director of the Alan Turing Institute, the UK's national institute for Data Science. He studied and did postdoctoral work at University of Pennsylvania, MIT (with Michael Jordan), the University of Toronto (with Geoffrey Hinton). His academic career includes concurrent appointments as one of the founding members of the Gatsby Computational Neuroscience Unit in London, and as a faculty member of CMU's Machine Learning Department for over 10 years. His research and studies span computer science, engineering, cognitive science and Neuroscience. Zoubin's current research interests include statistical machine learning, interpretable artificial intelligence, Bayesian optimisation, scalable inference, probabilistic programming, and building an automatic statistician (a project that won a Google Focused Research Award). He has published over 250 papers, which have received a total of over 37,000 citations (and has an h-index of 83). He has served as programme and general chair of the leading international conferences in machine learning: AISTATS, ICML, and NIPS. He was co-founder of Geometric Intelligence (now Uber AI Labs). In 2015, he was elected a Fellow of the Royal Society.

Doug Bemis

RESEARCH AREA

  • NLP
  • Deep Learning
  • Applications

Doug manages the many facets of research at AI Labs and directs the connection between innovation and application. He has served as CTO/Co-Founder for several startups, including Syntracts LLC (NLP processing), Windward Mark Interactive (video game development), and Geometric Intelligence, which became Uber AI Labs. Doug received a PhD from NYU in neurolinguistics, for work using magnetoencephalography to investigate the neural bases of semantic composition. This led to further post-graduate work at Neurospin in France with Stanislas Dehaene and then to the co-founding of Geometric Intelligence with Gary Marcus, Zoubin Ghahramani, and Ken Stanley.

Ken Stanley

RESEARCH AREA

  • Neuroevolution
  • Neural Networks
  • Evolutionary Computation

Before joining Uber AI Labs full time, Ken was an associate professor of computer science at the University of Central Florida (he is currently on leave). He is a leader in neuroevolution (combining neural networks with evolutionary techniques), where he helped invent prominent algorithms such as NEAT, CPPNs, HyperNEAT, and novelty search. His ideas have also reached a broader audience through the recent popular science book, Why Greatness Cannot Be Planned: The Myth of the Objective.

RESEARCH AREA

  • Probabilistic Machine Learning

Zoubin Ghahramani is Chief Scientist at Uber. He is also a Professor of Information Engineering at the University of Cambridge, and Deputy Academic Director of the Leverhulme Centre for the Future of Intelligence. Until recently he was also the founding Cambridge Director of the Alan Turing Institute, the UK's national institute for Data Science. He studied and did postdoctoral work at University of Pennsylvania, MIT (with Michael Jordan), the University of Toronto (with Geoffrey Hinton). His academic career includes concurrent appointments as one of the founding members of the Gatsby Computational Neuroscience Unit in London, and as a faculty member of CMU's Machine Learning Department for over 10 years. His research and studies span computer science, engineering, cognitive science and Neuroscience. Zoubin's current research interests include statistical machine learning, interpretable artificial intelligence, Bayesian optimisation, scalable inference, probabilistic programming, and building an automatic statistician (a project that won a Google Focused Research Award). He has published over 250 papers, which have received a total of over 37,000 citations (and has an h-index of 83). He has served as programme and general chair of the leading international conferences in machine learning: AISTATS, ICML, and NIPS. He was co-founder of Geometric Intelligence (now Uber AI Labs). In 2015, he was elected a Fellow of the Royal Society.

RESEARCH AREA

  • NLP
  • Deep Learning
  • Applications

Doug manages the many facets of research at AI Labs and directs the connection between innovation and application. He has served as CTO/Co-Founder for several startups, including Syntracts LLC (NLP processing), Windward Mark Interactive (video game development), and Geometric Intelligence, which became Uber AI Labs. Doug received a PhD from NYU in neurolinguistics, for work using magnetoencephalography to investigate the neural bases of semantic composition. This led to further post-graduate work at Neurospin in France with Stanislas Dehaene and then to the co-founding of Geometric Intelligence with Gary Marcus, Zoubin Ghahramani, and Ken Stanley.

RESEARCH AREA

  • Neuroevolution
  • Neural Networks
  • Evolutionary Computation

Before joining Uber AI Labs full time, Ken was an associate professor of computer science at the University of Central Florida (he is currently on leave). He is a leader in neuroevolution (combining neural networks with evolutionary techniques), where he helped invent prominent algorithms such as NEAT, CPPNs, HyperNEAT, and novelty search. His ideas have also reached a broader audience through the recent popular science book, Why Greatness Cannot Be Planned: The Myth of the Objective.

Jeff Clune

RESEARCH AREA

  • Deep reinforcement learning
  • Deep learning
  • Evolving neural networks (neuroevolution)
  • Computational biology
  • Generative models

Jeff is on leave from the University of Wyoming, where he is the Loy and Edith Harris Associate Professor in Computer Science and directs the Evolving AI Lab (http://EvolvingAI.org). He researches robotics and creating artificial intelligence in neural networks, either via deep learning or evolutionary algorithms. In the last three years a robotics paper he coauthored was on the cover of Nature, he won an NSF CAREER award, he received the Distinguished Young Investigator Award from the International Society for Artificial Life, and deep learning papers he coauthored were awarded oral presentations at NIPS, CVPR, ICLR, and an ICML workshop.

Noah Goodman

RESEARCH AREA

  • Probabilistic Modeling

In addition to working at Uber AI Labs, Noah is also an Associate Professor of Psychology, Computer Science, and Linguistics at Stanford University, where he runs the Computation and Cognition Lab. He studies the computational basis of natural and artificial intelligence, merging behavioral experiments with formal methods from statistics and programming languages. His research topics include language understanding, social reasoning, and concept learning, as well as applications of these ideas and enabling technologies such as probabilistic programming languages. Professor Goodman has published more than 150 papers in fields including psychology, linguistics, computer science, and mathematics.

Eli Bingham

RESEARCH AREA

  • Probabilistic Modeling

Eli is a research scientist working on probabilistic programming, approximate Bayesian inference, and grounded language understanding. He has previously worked on condensed matter physics, computational biology, climatology, multiscale dictionary learning, and deep learning for computer vision. In his spare time he hangs out in a lab tinkering with his nanopore DNA sequencer.

RESEARCH AREA

  • Deep reinforcement learning
  • Deep learning
  • Evolving neural networks (neuroevolution)
  • Computational biology
  • Generative models

Jeff is on leave from the University of Wyoming, where he is the Loy and Edith Harris Associate Professor in Computer Science and directs the Evolving AI Lab (http://EvolvingAI.org). He researches robotics and creating artificial intelligence in neural networks, either via deep learning or evolutionary algorithms. In the last three years a robotics paper he coauthored was on the cover of Nature, he won an NSF CAREER award, he received the Distinguished Young Investigator Award from the International Society for Artificial Life, and deep learning papers he coauthored were awarded oral presentations at NIPS, CVPR, ICLR, and an ICML workshop.

RESEARCH AREA

  • Probabilistic Modeling

In addition to working at Uber AI Labs, Noah is also an Associate Professor of Psychology, Computer Science, and Linguistics at Stanford University, where he runs the Computation and Cognition Lab. He studies the computational basis of natural and artificial intelligence, merging behavioral experiments with formal methods from statistics and programming languages. His research topics include language understanding, social reasoning, and concept learning, as well as applications of these ideas and enabling technologies such as probabilistic programming languages. Professor Goodman has published more than 150 papers in fields including psychology, linguistics, computer science, and mathematics.

RESEARCH AREA

  • Probabilistic Modeling

Eli is a research scientist working on probabilistic programming, approximate Bayesian inference, and grounded language understanding. He has previously worked on condensed matter physics, computational biology, climatology, multiscale dictionary learning, and deep learning for computer vision. In his spare time he hangs out in a lab tinkering with his nanopore DNA sequencer.

Jay Chen

RESEARCH AREA

  • Neuroevolution
  • Deep Learning

Before joining Uber AI Labs as a Software Engineer, Jay worked on Uber's dispatch and pricing algorithms. Prior to Uber, Jay optimized ads delivery in Newsfeed at Facebook.

Jonathan Chen

RESEARCH AREA

  • Probabilistic Modeling
  • Bayesian Inference
  • Probabilistic Programming

Jonathan is a researcher, focusing on using probabilistic graphical models to solve spatio-temporal problems. He holds an undergraduate degree from the University of Pennsylvania.

Edoardo Conti

RESEARCH AREA

  • Deep Reinforcement Learning

Edoardo is a machine learning researcher & engineer currently working on deep reinforcement learning. Prior to joining AI labs he worked on developing Uber’s dispatch and surge pricing algorithms. He is a graduate of MIT and Rutgers University where he studied math, computer science, and quantitative finance.

RESEARCH AREA

  • Neuroevolution
  • Deep Learning

Before joining Uber AI Labs as a Software Engineer, Jay worked on Uber's dispatch and pricing algorithms. Prior to Uber, Jay optimized ads delivery in Newsfeed at Facebook.

RESEARCH AREA

  • Probabilistic Modeling
  • Bayesian Inference
  • Probabilistic Programming

Jonathan is a researcher, focusing on using probabilistic graphical models to solve spatio-temporal problems. He holds an undergraduate degree from the University of Pennsylvania.

RESEARCH AREA

  • Deep Reinforcement Learning

Edoardo is a machine learning researcher & engineer currently working on deep reinforcement learning. Prior to joining AI labs he worked on developing Uber’s dispatch and surge pricing algorithms. He is a graduate of MIT and Rutgers University where he studied math, computer science, and quantitative finance.

Eric Frank

RESEARCH AREA

  • Neuroevolution

Before joining Uber AI Labs as a researcher, Eric invented AI oriented toys for Kite and Rocket Research. He was also a research assistant at the University of Rochester and makes art in his free time.

Martin Jankowiak

RESEARCH AREA

  • Approximate Bayesian Inference
  • Probabilistic Programming languages (PPLs)
  • Spatiotemporal modeling
  • Gaussian Processes

Martin is a former particle physicist whose interest in data and modeling goes back to the Large Hadron Collider. After physics stops at Stanford and Heidelberg, he became a Research Scientist at the Center for Urban Science and Progress at NYU with a focus on applied machine learning research. Martin then joined a small machine learning start-up (Geometric Intelligence) with the happy end result that he joined AI Labs in March 2017.

Theofanis Karaletsos

RESEARCH AREA

  • Probabilistic Modeling
  • Approximate Inference
  • Probabilistic Programming

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.

RESEARCH AREA

  • Neuroevolution

Before joining Uber AI Labs as a researcher, Eric invented AI oriented toys for Kite and Rocket Research. He was also a research assistant at the University of Rochester and makes art in his free time.

RESEARCH AREA

  • Approximate Bayesian Inference
  • Probabilistic Programming languages (PPLs)
  • Spatiotemporal modeling
  • Gaussian Processes

Martin is a former particle physicist whose interest in data and modeling goes back to the Large Hadron Collider. After physics stops at Stanford and Heidelberg, he became a Research Scientist at the Center for Urban Science and Progress at NYU with a focus on applied machine learning research. Martin then joined a small machine learning start-up (Geometric Intelligence) with the happy end result that he joined AI Labs in March 2017.

RESEARCH AREA

  • Probabilistic Modeling
  • Approximate Inference
  • Probabilistic Programming

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.

Joel Lehman

RESEARCH AREA

  • Neuroevolution
  • Deep Reinforcement Learning

Joel Lehman was previously an assistant professor at the IT University of Copenhagen, and researches neural networks, evolutionary algorithms, and reinforcement learning.

Rosanne Liu
Research Scientist

Rosanne obtained her PhD in Computer Science at Northwestern University, where she used neural networks to help discover novel materials. Months before graduation she started working as a machine learning researcher at a New York based startup Geometric Intelligence, which later became Uber AI Labs. She has a diverse background in optimization, automotive control, materials informatics and social media mining. She loves reading, standup comedies and playing with dogs.

Piero Molino

RESEARCH AREA

  • Natural language Processing

Piero received a PhD in Computer Science from the University of Bari, Italy,. He worked for Yahoo Labs in Barcelona on learning to rank, at IBM Watson in New York on Question Answering and at Geometric Intelligence on Grounded Language Understanding.

RESEARCH AREA

  • Neuroevolution
  • Deep Reinforcement Learning

Joel Lehman was previously an assistant professor at the IT University of Copenhagen, and researches neural networks, evolutionary algorithms, and reinforcement learning.

Rosanne obtained her PhD in Computer Science at Northwestern University, where she used neural networks to help discover novel materials. Months before graduation she started working as a machine learning researcher at a New York based startup Geometric Intelligence, which later became Uber AI Labs. She has a diverse background in optimization, automotive control, materials informatics and social media mining. She loves reading, standup comedies and playing with dogs.

RESEARCH AREA

  • Natural language Processing

Piero received a PhD in Computer Science from the University of Bari, Italy,. He worked for Yahoo Labs in Barcelona on learning to rank, at IBM Watson in New York on Question Answering and at Geometric Intelligence on Grounded Language Understanding.

Yunus Saatci

RESEARCH AREA

  • Probabilistic Machine Learning

Yunus did his PhD with Carl Rasmussen and Zoubin Ghahramani at the University of Cambridge. His research focused on scalable inference for Bayesian nonparametrics models, with a particular emphasis on Gaussian Processes. Since completing his PhD Yunus has applied machine learning to quite a variety of application domains, including high frequency trading, self-driving cars and trucks, natural language processing and even venture capital. He continues his adventures in the world of AI and ML at Uber, where probabilistic inference, deep learning and huge datasets meet to generate the next wave of innovation in the field.

Paul Szerlip

RESEARCH AREA

  • Probabilistic Programming

Paul Szerlip earned his PhD with Dr. Kenneth Stanley at the University of Central Florida focusing on open-source infrastructure for collaborative evolutionary software. This open-source platform enables researchers to quickly integrate crowd-sourced human contributions with automated algorithms, while making the results easily accessible online. His later research highlighted new ways to integrate neuroevolutionary techniques like HyperNEAT and Novelty Search into deep learning frameworks.

Jason Yosinski

RESEARCH AREA

  • Machine Learning

Jason Yosinski is a machine learning researcher and founding member of Uber AI Labs, where he uses neural networks and machine learning to build more capable and more understandable AI. He suspects scientists and engineers will build increasingly powerful AI systems faster than we can understand them, motivating much of his work on what has been called "AI Neuroscience"" -- an emerging field that may become increasingly important in the next several years. Mr. Yosinski was previously a PhD student and NASA Space Technology Research Fellow working at the Cornell Creative Machines Lab, the University of Montreal, the Caltech Jet Propulsion Laboratory, and Google DeepMind. His work on AI has been featured on NPR, Fast Company, the Economist, TEDx, and on the BBC.

RESEARCH AREA

  • Probabilistic Machine Learning

Yunus did his PhD with Carl Rasmussen and Zoubin Ghahramani at the University of Cambridge. His research focused on scalable inference for Bayesian nonparametrics models, with a particular emphasis on Gaussian Processes. Since completing his PhD Yunus has applied machine learning to quite a variety of application domains, including high frequency trading, self-driving cars and trucks, natural language processing and even venture capital. He continues his adventures in the world of AI and ML at Uber, where probabilistic inference, deep learning and huge datasets meet to generate the next wave of innovation in the field.

RESEARCH AREA

  • Probabilistic Programming

Paul Szerlip earned his PhD with Dr. Kenneth Stanley at the University of Central Florida focusing on open-source infrastructure for collaborative evolutionary software. This open-source platform enables researchers to quickly integrate crowd-sourced human contributions with automated algorithms, while making the results easily accessible online. His later research highlighted new ways to integrate neuroevolutionary techniques like HyperNEAT and Novelty Search into deep learning frameworks.

RESEARCH AREA

  • Machine Learning

Jason Yosinski is a machine learning researcher and founding member of Uber AI Labs, where he uses neural networks and machine learning to build more capable and more understandable AI. He suspects scientists and engineers will build increasingly powerful AI systems faster than we can understand them, motivating much of his work on what has been called "AI Neuroscience"" -- an emerging field that may become increasingly important in the next several years. Mr. Yosinski was previously a PhD student and NASA Space Technology Research Fellow working at the Cornell Creative Machines Lab, the University of Montreal, the Caltech Jet Propulsion Laboratory, and Google DeepMind. His work on AI has been featured on NPR, Fast Company, the Economist, TEDx, and on the BBC.

Xingwen Zhang

RESEARCH AREA

  • Neuroevolution
  • Deep Learning

Xingwen obtained his PhD in Operations, Information and Technology from Stanford University. His prior research focused on optimization, with papers on topics including the Vehicle Routing Problems. Before transferring to Uber's AI Labs, Xingwen worked in Map Services and Intelligent Dispatch. While in Map Services, he was one of the two engineers who first built a route planner customized for Uber's business. He later worked in Map Matching, improving both the accuracy of map-matched traces and the computational efficiency of the map matching algorithm. While in Intelligent Dispatch, he focused on matching and routing joint optimization, which is now powering products like UberEATS and UberPOOL.

RESEARCH AREA

  • Neuroevolution
  • Deep Learning

Xingwen obtained his PhD in Operations, Information and Technology from Stanford University. His prior research focused on optimization, with papers on topics including the Vehicle Routing Problems. Before transferring to Uber's AI Labs, Xingwen worked in Map Services and Intelligent Dispatch. While in Map Services, he was one of the two engineers who first built a route planner customized for Uber's business. He later worked in Map Matching, improving both the accuracy of map-matched traces and the computational efficiency of the map matching algorithm. While in Intelligent Dispatch, he focused on matching and routing joint optimization, which is now powering products like UberEATS and UberPOOL.

Join us
We are located in San Francisco and are looking for talented researchers and engineers of all levels. Our work spans many techniques including deep learning, probabilistic modeling, Bayesian optimization, evolution, reinforcement learning, and more!
AI Labs Research Scientist
Research scientists create the next generation of artificial intelligence algorithms to help Uber improve the lives of millions of people worldwide.
AI Labs Data Scientist
Data Scientists in AI Labs work with Research Scientists to create, test, and deploy the next generation of artificial intelligence algorithms to help Uber improve the lives of millions of people worldwide.
Bayesian Optimization Research Scientist
We are seeking an expert in Bayesian optimization (and related areas such as multi-arm bandit problems and optimal experiment design) to magnify the efficiency of Uber.
Get in touch
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