- We are accepting applications until January 28, 2019.
- Our team will review applications on a rolling basis and interviews will begin in November 2018. It is in your best interest to submit your application early.
- The Google AI Residency Program will have 3 start dates over the course of 5 months, from June to October 2019. Exact dates are yet to be determined.
About the Program
The Google AI 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.
Please note that by applying to this job, you may be considered for all of the locations: Bay Area (Mountain View and San Francisco), New York City, Cambridge (Massachusetts), Montreal and Toronto, Canada, 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.
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.
For more information click "LINK TO ORIGINAL" below.