Research Scientist Intern, Muse RL and Agents Team
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Develop novel state-of-the-art agentic AI algorithms and corresponding systems, leveraging machine learning and reinforcement learning techniques.
Conduct research on agentic LLMs, agentic RL environments, LLM post-training, and related topics.
Analyze and improve the efficiency, scalability, and stability of agentic AI algorithms and deployed systems.
Advance the science and technology of intelligent, agentic machines capable of reasoning, tool use, and personalized interactions.
Collaborate with researchers and cross-functional partners, including communicating research plans, progress, and results.
Disseminate research results through publications, presentations, and open source contributions.
When applicable, contribute to research that can be applied to Meta product development.
Currently has or is in the process of obtaining a Masters 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
Experience building systems based on machine learning and/or deep learning methods
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, ICML, ICLR, ACL, EMNLP, CVPR, ICCV, ECCV, or similar
Experience working and communicating cross functionally in a team environment
Experience in advancing agentic AI techniques, including core contributions to open source libraries and frameworks in agentic LLMs or RL environments
Publications or experience in agentic AI, LLMs, reinforcement learning, reasoning/coding, optimization, computer science, statistics, applied mathematics, or data science
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 with deep learning frameworks
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