Google AI Residency Program

The Google AI Residency Program is a 18-month research training role designed to jumpstart or advance your career in machine learning research.

The Google AI Residency Program was created in 2015 with the goal of training and supporting the next generation of deep learning researchers. With machine learning fast becoming a critical area for a broad range of applications, we recognized the need to evolve our research goals and expand beyond deep learning to include a breadth of machine learning subfields. 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.

Residents will have the opportunity to be mentored by distinguished scientists and engineers from various teams within Google AI, and work on real-world machine learning problems and applications. In addition, they will also have the opportunity to collaborate and partner closely with various research groups across Google and Alphabet.

Meet some of our current residents

By drawing on Google's state-of-the-art resources and research experience, we provide Residents in the program the skills that will enable them to tackle some of the world's greatest machine learning challenges. Meet some of our current Residents in the cohort!

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Learn more about life as an AI Resident

Our Residents bring a diverse range of backgrounds and experiences from all over the world. Learn more from Residents past and present about what it’s like to be in the program.

Google AI Residency Program Highlights

An inside look at some highlights from the first three classes of the Google AI Residency Program.

My Path to Google: Jasmine Collins, Google Brain Residency Alumna

These are real stories from Googlers, interns, and alumni highlighting how they got to Google, what their roles are like, and even some tips on how to prepare for interviews.

Google Brain Residency Program — 7 months in and looking ahead

In October 2015 we launched the Google Brain Residency, a 12-month program focused on jumpstarting a career for those interested in machine learning and deep learning research.

My Path to Google: Julius Adebayo, Google AI Resident

These are real stories from Googlers, interns, and alumni highlighting how they got to Google, what their roles are like, and even some tips on how to prepare for interviews.

The Google Brain Residency Program — One Year Later

Over the past year, the Residents familiarized themselves with the literature, designed and implemented experiments at Google scale, and engaged in cutting edge research.

Collin Raffel's year at Brain

I've spent the past year doing the Google Brain Residency Program. In this blog post I'll describe what the residency was like, what I worked on while here, and what I'm doing next.

An AI Resident at work: Suhani Vora and her work on genomics

Suhani describes her work as an AI Resident, her typical day, and how AI can help transform the field of genomics

Resident papers accepted to ICML, 2019

Residents are encouraged to read papers, work on research projects, and publish in top-tier venues. These are the Resident papers that were accepted to ICML this year.

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The Value Function Polytope in Reinforcement Learning

We establish geometric and topological properties of the space of value functions in finite state-action Markov decision processes. Our main contribution is the characterization of the nature of its shape: a general polytope \cite{aigner2010proofs}. To demonstrate this result, we exhibit several properties of the structural relationship between policies and value functions including the line...

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The Value Function Polytope in Reinforcement Learning

Robert Dadashi, Adrien Ali Taiga, Nicolas Le Roux, Dale Schuurmans, Marc G. Bellemare

Proceedings of the 36th International Conference on Machine Learning, ICML (2019), pp. 1486-1495 (to appear)

Learning to Generalize from Sparse and Underspecified Rewards

We consider the problem of learning from sparse and underspecified rewards, where an agent receives a complex input, such as a natural language instruction, and needs to generate a complex response, such as an action sequence, while only receiving binary success-failure feedback. Such success failure rewards are often underspecified: they do not distinguish between purposeful and accidental...

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Learning to Generalize from Sparse and Underspecified Rewards

Rishabh Agarwal, Chen Liang, Dale Schuurmans, Mohammad Norouzi

Proceedings of the 36th International Conference on Machine Learning, PMLR, Long Beach, California, USA (2019), pp. 130-140

The Effect of Network Width on Stochastic Gradient Descent and Generalization: an Empirical Study

We investigate how the behavior of stochastic gradient descent is influenced by model size. By studying families of models obtained by increasing the number of channels in a base network, we examine how the optimal hyperparameters---the batch size and learning rate at which the test error is minimized---correlate with the network width. We find that the optimal "normalized noise scale," which...

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The Effect of Network Width on Stochastic Gradient Descent and Generalization: an Empirical Study

Daniel S. Park, Jascha Sohl-dickstein, Quoc V. Le, Sam Smith

ICML (2019) (to appear)

AI Residency FAQ

Have questions not covered in the FAQs? Feel free to forward your questions to ai-residency@google.com.

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 machine 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 machine 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 machine learning.

Where is the Residency Program based?

The Google AI Residency Program is primarily based in Mountain View, California. Residents will also have the opportunity to be based in New York, Cambridge (Massachusetts), Seattle (Washington), Montreal, Toronto, Zurich (Switzerland), Berlin (Germany), Accra (Ghana) and Tel Aviv (Israel) depending on project fit and availability. Your recruiter will work with you to determine the best location for your work, though please also let them know if you have a location preference.

How do I apply and what does the application timeline look like?

Applications for the program are now closed and will re-open this coming fall for the 2021 program. We will post the updated timeline here when our application re-opens.

I’m interested in applying! What documents do I need to prepare in order to submit my application correctly?

Check out “Completing the application” section in our FAQs page (g.co/airesidency/faqs) for instructions and a list of required documents.

What does the program curriculum look like?

Google AI Residents will spend the first several weeks of the program attending Google Orientation sessions and learning how research is conducted at Google by completing AI Residency curriculum and through direct collaboration with their assigned Orientation Mentor and other researchers. Each resident will then be assigned a short project to be completed within two weeks, 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 shortly after your residency begins. This will give residents the opportunity to interact with various Google researchers and learn more about the different types of machine learning work across Google AI.

Mentor & Project selection

We'll work to help you find a mentor or mentors whose research-interests are aligned with yours and together with your mentor(s), you will formulate a longer-term project that's exciting to both of you. You may propose your own project ideas as well. Each resident will have the opportunity to work with more than one mentor at a time, and residents will have the opportunity to work with a variety of mentors over the course of the residency. We encourage residents to be open-minded when considering mentors; sometimes the best match to help you achieve your research goals won’t be the first mentor you had in mind.

I have more questions about the program! Where should I go?

Check out our comprehensive list of program FAQs at g.co/airesidency/faqs.