Research Internship

The Research Intern’s role is responsible for building AI systems that can perform previously impossible tasks or achieve unprecedented levels of performance. This requires good engineering (for example: designing, implementing, and improving a massive-scale distributed machine learning system), writing bug-free machine learning code (surprisingly difficult!), and building the science behind the algorithms employed. In all the projects this role pursues, the ultimate goal is to push the field forward.The most outstanding deep learning results are increasingly attained at massive scale, and these results require engineers who are comfortable working in a large distributed systems. We expect engineering to play a key role in most major advances in AI of the future.

You should apply if you are a computer scientist who is fairly comfortable with unsupervised learning, natural language processing and the basics of data science. Experience in security is a bonus. You are not expected to have mastery of deep learning — the problems we pursue are always at the edge of the state of the art, which means that nobody’s an expert.

Internships can last between 2-6 months and operate with a focus on developing cutting edge research publishable at a top tier academic conference (NIPS, ICLR, ICML, ACL).

Minimum qualifications:

  • Studying an MS degree or equivalent 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.

Preferred qualifications:

  • Studying a PhD or equivalent in a STEM field such as Computer Science, Mathematics, or Statistics.
  • 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.