We are committed to advancing the field of artificial intelligence by making fundamental advances in technologies to help interact with and understand our world. We are seeking individuals passionate in areas such as deep learning, reinforcement learning, planning, large language models, and generative modeling. Our interns have an opportunity to make core algorithmic advances and apply their ideas at an unprecedented scale.Our internships are twelve (12) to twenty-four (24) weeks long and we have various start dates throughout the year.

Research Scientist Intern, Reinforcement Learning and Large Language Models (PhD Responsibilities:

• Develop novel state-of-the-art machine learning algorithms and corresponding systems, leveraging various deep learning techniques.
• Analyze and improve efficiency, scalability, and stability of corresponding deployed algorithms.
• Perform state of the art research to advance the science and technology of Machine Learning and Artificial Intelligence.
• Collaborate with researchers and cross-functional partners including communicating research plans, progress, and results.
• Publish research results and contribute to research that can be applied to Meta product development.

• Currently has or is in the process of obtaining a PhD degree in Machine Learning, Artificial Intelligence, Computer Science, Reinforcement Learning, Mathematics, or relevant technical field.
• Must obtain work authorization in 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 with deep learning frameworks such as Pytorch or Tensorflow.

• 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 such as NeurIPS, ICLR, ICML, AAAI, AISTATS, IJCAI or similar.
• Demonstrated experience and self-driven motivation in solving analytical problems using quantitative approaches.
• ML/ AI research and/ or work experience in deep learning, reinforcement learning, generative models, LLMs, planning.
• Demonstrated software engineer experience via an internship, work experience, coding competitions, or widely used contributions in open source repositories (e.g. GitHub).
• Experience working and communicating cross functionally in a team environment.