Jeff joined Google in 1999 and is currently a Google Senior Fellow in Google's Research Group, where he leads the Google Brain team, Google's deep learning research team in Mountain View. He has co-designed/implemented five generations of Google's crawling, indexing, and query serving systems, and co-designed/implemented major pieces of Google's initial advertising and AdSense for Content systems. He is also a co-designer and co-implementor of Google's distributed computing infrastructure, including the MapReduce, BigTable and Spanner systems, protocol buffers, LevelDB, systems infrastructure for statistical machine translation, and a variety of internal and external libraries and developer tools. He is currently working on large-scale distributed systems for machine learning. He received a Ph.D. in Computer Science from the University of Washington in 1996, working with Craig Chambers on compiler techniques for object-oriented languages. He is a Fellow of the ACM, a Fellow of the AAAS, a member of the U.S. National Academy of Engineering, and a recipient of the Mark Weiser Award and the ACM-Infosys Foundation Award in the Computing Sciences.
Rajat Monga leads TensorFlow at the Google Brain team, where he's interested in pushing research forward and democratizing it. Prior to Google, Rajat built out labs and operations at Attributor as the chief architect and director of engineering. A veteran developer, Rajat has worked at eBay, Infosys, and a number of startups.
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Megan Kacholia is an Engineering Director on TensorFlow/Brain, and a long-time Googler. She specializes in working on large-scale, distributed systems, and finding ways to tune and improve performance in such environments.
Human Software Engineer
Chris is a compiler engineer working on compiling TensorFlow with XLA. His current passions include unlocking peak performance, building systems, and those little coconut-filled chocolates that show up around Valentine's day. He is a firm believer that ML folk and HPC folk should be BFF.
Todd is a software engineer on the Google Brain team, with interests in systems infrastructure, programming abstractions, and machine learning. Within Google he has also worked on web search, cluster management, and internet-of-things infrastructure. Prior to Google he has worked in the areas of speech recognition, speaker identification, and distributed infrastructure for such systems.
Developer Programs Engineer
Wolff Dobson is a Developer Programs Engineer at Google specializing in machine learning and games. Before Google, he worked as a game developer for 12 years, including writing AI for the NBA2K series and helping design the Wii Motion Plus. He has a PhD in artificial intelligence from Northwestern University.
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Martin Wicke is a software engineer on Google's TensorFlow team. His main interest is making cutting edge machine learning infrastructure available to the world. After completing a PhD in computer graphics at ETH Zurich, Martin Wicke worked on simulation at Stanford University and UC Berkeley. Before joining Google, he worked at startups tackling problems in engineering, energy, robotics, and AI.
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François is a software engineer working with Google Brain and Machine Perception. He does deep learning research in computer vision and automated theorem proving. He is the maintainer of the Keras deep learning framework.
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Software EngineerDandelion is a software engineer at Google Brain, and the original developer of TensorBoard. They are interested in how data visualization can empower machine learning research, and in the social and economic impacts of machine learning. They are also an avid fusion dancer.
Software EngineerDerek is a Software Engineer on the TensorFlow team. Since joining Google two years ago, he has been responsible for many different parts of the system, including the open-source distributed runtime, and TensorFlow on Windows. When he's not helping to build TensorFlow, Derek can be found on Stack Overflow, answering your questions about how to use it.
Product ManagerLily is a non-practicing physician and product manager for a team that works on applying deep learning and other Google’s technologies and expertise to increase access, accuracy, and clinical utility of medical imaging, such as retinal imaging. Before Google, Lily was a product manager at Doximity, the "linkedin" for physicians, and a co-founder of Nano Precision Medical (NPM), a medical device startup that is developing a small implantable continuous drug delivery device. Lily completed her M.D. and Ph.D. in Bioengineering at the University of California, San Francisco and Berkeley. Lily received her B.S. with honors and distinction in Chemical Engineering from Stanford University.
Jonathan is a software engineer working on TensorFlow. His primary interests are machine learning and distributed systems. Previously, he worked as tech lead of data infrastructure at Dropbox and tech lead/manager of logs analysis infrastructure at Google.
Staff Software Engineer
Pete is the technical lead for TensorFlow on mobile and embedded. He is the former CTO of Jetpac, which was acquired in 2014.
Zak Stone is the Product Manager for TensorFlow on the Google Brain team. He contributes to product strategy, collaborates with other teams across Google, and enjoys interacting with TensorFlow's vibrant open-source community. Prior to joining Google, Zak founded a mobile-focused deep learning startup that was acquired by Apple. While at Apple, Zak contributed to the on-device face identification technology in iOS 10 and macOS Sierra that was announced at WWDC 2016.
Noah is Tech Lead of TensorFlow Serving, the open source ML serving system running many production models at Google. In his Google career he has helped build the systems powering YouTube, Blogger, Google+, and Hangouts. Prior to Google he’s worked on projects as large as Boeing-Jeppesen flight planning software and as small as Color Labs, a 25 person startup he co-founded in the mobile space. Noah holds a BS in EECS from UC Berkeley, is a private pilot, and is on a mission to find the best burrito in Silicon Valley.
Software EngineerHeng-Tze Cheng is a technical lead manager and staff software engineer at Google Research. Heng-Tze founded the Wide & Deep Learning project in TensorFlow, and has worked on large-scale machine learning platforms that are widely used for retrieval, ranking, and recommender systems. Prior to joining Google, Heng-Tze received his Ph.D. from Carnegie Mellon University in 2013 and B.S. from National Taiwan University in 2008. His personal mission is to empower the world to drive positive changes with beautifully intelligent technology.
Ashish is actively involved in extending and applying Tensorflow to improve core Google products like Search and Ads. Before this, he spent many years building Search Ads foundations, from machine learnt signals to the ads auction, and also created a Google-wide live traffic experimentation framework.
Eugene is a software engineer on the Applied Machine Intelligence team. He primarily works on TensorFlow infrastructure, and is the maintainer of the RNN and new seq2seq libraries. His research interests include variational inference and RL, with applications in speech recognition and synthesis, and biomedical time series.
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