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Research Intern - Human-oriented Artificial Intelligence

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Microsoft

Nov 28, 2023

Applications are closed

  • Internship
    Full-time
    Summer Internship
  • Research & Development
    Software Engineering
  • New York

Requirements

  • Required Qualifications:
  • Currently enrolled in a PhD program
  • Have a demonstrated publication record in machine learning/AI (including but not limited to ICLR/NeurIPS/ICML)
  • Other Requirements:
  • Research Interns are expected to be physically located in their manager’s Microsoft worksite location for the duration of their internship.
  • In addition to the qualifications above, you’ll need to submit a minimum of two reference letters for this position. After you submit your application, a request for letters may be sent to your list of references on your behalf. Note that reference letters cannot be requested until after you have submitted your application, and furthermore, that they might not be automatically requested for all candidates. You may wish to alert your letter writers in advance, so they will be ready to submit your letter.
  • Preferred Qualifications:
  • Experience with Pytorch
  • Experience in related fields such as cognitive science, neuroscience, or behavioral psychology

Responsibilities

  • Research Interns put inquiry and theory into practice. Alongside fellow doctoral candidates and some of the world’s best researchers, Research Interns learn, collaborate, and network for life. Research Interns not only advance their own careers, but they also contribute to exciting research and development strides. During the 12-week internship, Research Interns are paired with mentors and expected to collaborate with other Research Interns and researchers, present findings, and contribute to the vibrant life of the community. Research internships are available in all areas of research, and are offered year-round, though they typically begin in the summer.
  • This work will involve extensive design, development, and execution of deep learning pipelines, including transformer-based models. A core goal is to create generative foundation models of human behavior, analogous to what Large Language Models (LLMs) do for text. A significant emphasis will be placed on interpreting and steering these models via human-understandable concepts. We will collaborate deeply with the larger Machine Learning group at Microsoft Research New York City.

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