Facebook AI Research Residency Program 2018

⏲ Jan 16, 2018, 7:18 AM
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The Facebook AI Research (FAIR) Residency Program is a one-year research training program with Facebook’s AI Research group, designed to give you hands-on experience of machine learning research. The program will pair you with a senior researcher or engineer in FAIR, who will act as your mentor. Together, you will pick a research problem of mutual interest and then devise new deep learning techniques to solve it. We also encourage collaborations beyond the assigned mentor. The research will be communicated to the academic community by submitting papers to top academic venues (NIPS, ICML, ICLR, CVPR, ICCV, ACL, EMNLP etc.), as well as open-source code releases. Visit the FAIR research page for examples of research performed in FAIR . The AI research residency experience is designed to prepare you for graduate programs in machine learning, or to kickstart a research career in the field. This is a full-time program that cannot be undertaken in conjunction with university study or a full-time job. We encourage applications from people who have a strong technical background and are passionate about AI research. Prior experience in machine learning is certainly a strength but we seek people from a diverse range of backgrounds, including areas ostensibly unrelated to machine learning such as (but not limited to) math, physics, finance, economics, linguistics, computational social science, and bioinformatics. Accepted research residents will be based in Facebook Menlo Park and New York locations. If a candidate requires a visa to work in the US, we will explore what options are available once they are accepted onto the program.

Resident Responsibilities

Learn how to perform research in deep learning and AI.
Understand prior work and existing literature.
Work with research mentor(s) to identify problem(s) of interest and develop novel AI techniques.
Translate ideas into practical code (in frameworks such as PyTorch, Caffe 2).
Write up research results in the form of an academic paper and submit to a top conference in the relevant area.

Eligibility Requirements

Bachelors degree in a STEM field such as Mathematics, Statistics, Physics, Electrical Engineering, Computer Science, or equivalent practical experience.
Completed coursework in: Linear Algebra, Probability, Calculus, or equivalent.
Coding experience in a general-purpose programming language, such as Python or C/C++.
Familiarity with a deep learning platform such as PyTorch, Caffe, Theano, or TensorFlow.
Ability to communicate complex research in a clear, precise, and actionable manner.

Preferred Qualifications

Research experience in machine learning or AI (as established for instance via publications and/or code releases).
Significant contributions to open-source projects, demonstrating strong math, engineering, statistics, or machine learning skills.
A strong track record of scholastic excellence.

Application Requirements

To apply to the 2018 Facebook AI Residency Program, you will need to complete the application and submit the following items:

CV/Resume (including links to GitHub, professional webpages, publications, or blogposts as applicable)
Personal statement
Academic grade transcript

Note: All materials must be in PDF format.

CV/Resume:

Please include details of: (i) research you have undertaken and publications that resulted; (ii) any Machine Learning competitions you participated in and their outcomes; (iii) details of significant coding experience and links to any open-source repositories. Please try to focus on impact.

Personal statement:

In a maximum of 2 pages, describe why you wish to participate in the program. Convey your previous experience in AI, interest in research, and long-term plans. We do not require a specific format, but encourage you to value readability (e.g. not go below 10 point fonts and 1 inch margins) and use your best judgment.

Grade transcript:

Include transcripts for all degrees completed. If applying from a non-US school, please append a conversion of your grades to US GPA equivalent (this tool may be helpful: https://www.foreigncredits.com/resources/gpa-calculator/, although this should not be considered an endorsement).