Google AI Residency Program
The Google AI Residency Program — previously known as the Google Brain Residency Program — is a 12-month research training role designed to jumpstart or advance your career in machine learning research. Residents will be embedded alongside distinguished scientists and engineers from an option of research teams, choosing placements within the Google Brain team, Machine Perception, or Google AI New York. In addition, they will also have the opportunity to collaborate and partner closely with various research groups across Google and Alphabet, including Google Accelerated Sciences, X, and Google Cloud AI.
The program was created in 2015 with the goal of training and supporting the next generation of deep learning researchers. With deep learning and other machine learning subfields fast becoming a critical area for a broad range of applications, people from a wide range of disciplines are beginning to realize the importance and impact of this area of research. With growing interest in the field, there is a corresponding need for researchers with hands-on experience in machine learning techniques and methodologies.
Since launch, we have welcomed two cohorts of Residents, and we’re now expanding the program’s scope to a broader group of teams doing machine learning research. By drawing on Google's state-of-the-art resources and research experience, we provide Residents on the program the skills that will enable them to tackle some of the world's greatest machine learning challenges.
Applications for the 2018 program are currently open! Check out submission details and apply at g.co/airesidency/apply. Applications close on January 8th, 2018.
Some of our current residents
Kathryn RoughBefore joining the residency program, I had completed my doctoral training in epidemiology and was doing research on prescription drug safety and effectiveness. The residency has given me the incredible opportunity to be part of an interdisciplinary team working to figure out how deep learning can be applied to help solve problems in health and medicine. It has been truly unique learning experience and I feel very fortunate to be surrounded by both leaders in the field and a cohort of amazing residents.
Jacob BuckmanAfter finishing my Master's degree in computer science at Carnegie Mellon's Language Technologies Institute, I joined the residency program for the opportunity to dive into deep learning research, especially outside of my focus area of NLP. I'm continually inspired by the brilliant researchers I am able to work with, and having access to all of Google's resources is nothing short of amazing. During my time as a resident, I've had the opportunity to work on projects as diverse as resisting adversarial examples for classification, generative models for text and images, and even a little language modeling. The residency program has helped me grow as a researcher and an individual, and I'm excited to see where it takes me in the future.
Ishaan GulrajaniI dropped out of college to start a startup, worked as a deep learning researcher at the University of Montreal, and now I’m a resident on the program. My current research interests are understanding how to build and evaluate algorithms that can learn about the world from unlabeled data. The residency offers me autonomy to work on the problems I think are most important, a chance to collaborate with very talented colleagues, and access to lots of computing resources. I’m very happy here.
Harini KannanI joined the residency straight after school; I had just completed my Bachelor’s and Master’s degrees in computer science at MIT. For my master’s thesis, I did research in computer vision under Professor Antonio Torralba, working on creating a deep learning model for gaze tracking. I enjoyed the taste of research I got during my Master’s degree, so I applied to the residency to see what it would be like to pursue it further. So far, my experience on the residency has been amazing. It’s been a great immersive research environment with many talks, reading groups, and informal discussions with other researchers. The compute capability makes it easy to run large scale experiments. I’m excited to learn as much as I can over the next year and explore all of the opportunities around me!
Sara HookerAfter a few years working in industry, I joined the residency to work on research that explores problems like algorithm transparency, security and privacy. These different areas all attempt to understand the implications of how algorithms perform after being deployed to the wild. I grew up in Africa and immediately prior to program, taught a machine learning course in Nairobi, Kenya. I recommend the residency as an incredible opportunity to work on problems that have a real impact and have access to a rich ecosystem of researchers and computing resources.
Sam SmithAfter finishing a PhD in Theoretical Physics at the University of Cambridge, I joined an AI startup in London to help them develop a medical chatbot. A year later I moved to the US for the Brain Residency. Currently I’m studying the workhorse of optimization, Stochastic Gradient Descent; developing new insights to train more accurate models with fewer gradient updates. The environment here is fantastic: I have complete control over my research, great mentors, and I’m surrounded by enthusiastic talented people. I could not recommend the program more highly!
Some Residency papers accepted to NIPS, 2017
- Bridging the Gap Between Value and Policy Based RL Ofir Nachum, Mohammad Norouzi, Kelvin Xu, Dale Schuurmans. NIPS (2017)
- Filtering Variational Objectives Chris J Maddison, Dieterich Lawson, George Tucker, Nicolas Heess, Mohammad Norouzi, Andriy Mnih, Arnaud Doucet, Yee Whye Teh. NIPS (2017)
- Investigating the learning dynamics of deep neural networks using random matrix theory Jeffrey Pennington, Sam Schoenholz, Surya Ganguli. NIPS (2017)
- REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models George Tucker, Andriy Mnih, Chris J. Maddison, Dieterich Lawson, Jascha Sohl-Dickstein. NIPS (2017)
- SVCCA: Singular Vector Canonical Correlation Analysis for Deep Understanding and Improvement Maithra Raghu, Justin Gilmer, Jason Yosinski, Jascha Sohl-Dickstein. NIPS (2017)
AI Residency FAQ
Who should apply to the Google AI Residency program?
Ideal candidate either has a degree (BS, MS or PhD) or equivalent experience in STEM field such as CS, Math or Statistics. Having said that, we highly encourage candidates with non-traditional backgrounds and experiences from all over the world to apply to our program. Most importantly we are looking for individuals who are motivated to learn and have a strong interest and passion for deep learning research.
What can I expect from the program?
The residency program is similar to spending a year in a Master's or PhD program in deep learning. Residents are expected to read papers, work on research projects, and encouraged to publish in top-tier venues. By the end of the program, residents are expected to gain significant research experience in deep learning.
Where is the Residency Program based in?
The program is primarily be based in Mountain View, California, and as of 2018 we are expanding our program to several new locations. Depending on project fit and team needs, Residents will have the opportunity to work in other locations such as New York, Cambridge (Massachusetts), Montreal and Toronto.
How do I apply and what does the application timeline look like?
Applications open October 2nd, 2017 through to January 8th, 2018. We encourage you to have all relevant documentations as outlined on g.co/airesidency/apply prepared and submitted before the closing date. Interviews will take place from February to March and successful candidates will be notified by early April.
What does the program curriculum look like?
Google AI Residents will spend the first two weeks of the program going through the Google Orientation sessions interlaced with introductory deep learning classes. Each resident will then be assigned a short project to be completed within a week, during which longer term project and mentor assignments will take place in tandem.
How does the project and mentor assignment process work?
Project and mentor assignment will take place a few weeks after the program kicks off. This will give residents the opportunity to interact with various team members within research and learn more about what work the team is passionate about.Project assignment
Projects chosen should ideally be a combination of short term and longer term projects. When choosing projects, residents will have the flexibility to select from a list of pre-proposed projects or propose their own ideas.Mentor assignment
Mentors will be assigned based on the projects a resident decides to undertake. Each resident will have the opportunity to work with more than one mentor at a time, and mentors will rotate depending on the project lifecycle.
I'm an applicant who needs US work authorization. Will Google sponsor my visa?
Google will sponsor visas for eligible applicants. We'll work with accepted applicants to determine the best visa options available.
Have questions not covered in the FAQs?
Feel free to forward your questions to email@example.com.