Posted on :
19 Oct, 2018
19 Oct, 2018
The Google AI Residency Program (formerly known as the Google Brain Residency Program) is a 12-month role designed to advance your career in machine learning research. The goal of the residency is to help residents become productive and successful AI researchers.
As part of this program, Residents collaborate with distinguished scientists from various Google AI teams working on machine learning applications and problems. Residents have the opportunity to do everything from conducting fundamental research to contributing to products and services used by billions of people. We also encourage our Residents to publish their work externally. Take a look at the impactful research done by earlier cohorts.
The AI Residency Program will continue to be based out of the Bay Area (Mountain View and San Francisco), as well as New York City, Cambridge (Massachusetts), Montreal and Toronto, Canada. In 2019, we will see the program expand to Seattle (Washington State), Accra (Ghana), Tel Aviv (Israel) and Zurich (Switzerland). Residents are placed based on interest, project fit, location preference and team needs. All are expected to work on site.
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We encourage candidates from all over the world to apply. You may have research experience in another field (e.g. human-computer interaction, mathematics, physics, bioinformatics, etc.) and want to apply machine learning to this area, or have limited research experience but a strong desire to learn more. Current students will need to graduate from their degree program (BS/MS/PhD) before the residency begins. If a candidate requires work authorization for a location, Google will explore the available options on a case-by-case basis.
Your application should show evidence of proficiency in programming and in prerequisite courses (e.g., machine learning, user-centered interfaces or applications, data science, mathematical analysis). This can be demonstrated through links to open-source projects, notable performances in competitions, publications and blog posts, or projects that showcase implementation of one or more novel learning algorithms.
To start the application process, click on the “Apply” button on this page and provide the required materials in the appropriate sections (PDFs preferred):
1. In the “Resume Section:” attach an updated CV or resume.
2. In the “Cover letter/other notes Section:” Copy and paste a cover letter which must include answers to the following questions:
What are your primary research interests and why do you think they are important?
How would participating in the AI Residency help you to explore your research interests and achieve your goals?
Give an example of an open-ended research question or project you’ve worked on. What made it challenging and how did you overcome those challenges? Alternatively, summarize and critique a machine learning paper you have read that you found interesting.
**Although cover letters are optional for most job applications at Google (as noted on the website), it is a mandatory component for this application. Complete applications (cover letters included) will be prioritized for review over incomplete applications.
Work with research mentors to formulate research project(s) and/or novel application(s) of machine learning.
Conduct research and publish it in competitive venues.
Implement algorithms, experiments and/or human-computer interfaces using frameworks such as TensorFlow.
Learn and understand a large body of research in machine learning algorithms.
BS degree or equivalent practical experience in a STEM field such as Computer Science, Mathematics, or Statistics.
Completed coursework in statistics, algorithms, calculus, linear algebra, or probability (or their equivalent).
Experience with one or more general purpose programming languages, including but not limited to: Python or C/C++
Experience with machine learning; or applications of machine learning to NLP, human-computer interaction, computer vision, speech, computer systems, robotics, algorithms, optimization, on-device learning, social networks, economics, information retrieval, journalism, or health care.
Research experience in machine learning or deep learning (e.g. links to open-source work or link to novel learning algorithms).
Open-source project experience that demonstrates programming, mathematical, and machine learning abilities and interests.