Research Scientist Intern, Llama Language Post-Training

3 weeks ago

Menlo Park, California, United States META Full time

Meta Research Scientist Intern, Llama Language Post-Training

We are seeking a highly motivated Research Scientist Intern to join our Llama post-training language team at Meta. As a Research Scientist Intern, you will have the opportunity to make core algorithmic advances and apply your ideas at an unprecedented scale.

Responsibilities:

  • Develop state-of-the-art generative AI algorithms and data to help more effectively align our LLMs during post-training stages.
  • Improve the performance of our foundational Llama models, which have wide open source adoption.
  • Based on the project, help analyze and improve efficiency, scalability, and stability of corresponding deployed algorithms.
  • Collaborate with researchers and cross-functional partners, including communicating research plans, progress, and results.
  • Disseminate research results.
  • When applicable, contribute to research that can be applied to Meta product development.

Requirements:

  • Currently has or is in the process of obtaining a Ph.D. degree in Computer Science, Artificial Intelligence, Generative AI, or a relevant technical field.
  • Must obtain work authorization in the country of employment at the time of hire and maintain ongoing work authorization during employment.
  • Experience with Python, C++, C, Java, or other related languages.
  • Experience building systems based on machine learning and/or deep learning methods.

Preferred Qualifications:

  • Intent to return to the degree program after the completion of the internship/co-op.
  • Proven track record of achieving significant results, as demonstrated by grants, fellowships, patents, as well as first-authored publications at leading workshops or conferences.
  • Experience working and communicating cross-functionally in a team environment.
  • Experience in advancing AI techniques, including core contributions to open source libraries and frameworks in computer vision.
  • Experience solving analytical problems using quantitative approaches.
  • Experience setting up ML experiments and analyzing their results.
  • Experience manipulating and analyzing complex, large-scale, high-dimensionality data from varying sources.
  • Experience in utilizing theoretical and empirical research to solve problems.
  • Experience training and aligning LLMs.

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