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Meta is seeking Research Interns to join our AI Solutions and Automation (ASA) Team. The ASA AI Exploration Team is a dynamic, applied research group focused on rapid exploration, post-training, and deployment of SOTA GenAI techniques. We are seeking individuals passionate in areas such as post-training particularly LLM post-training using reinforcement learning and reasoning, Agent post-training and long-horizon alignment, speculative decoding, Test-time scaling and adaptive compute. Our interns have an opportunity to work on high-impact projects, contribute to the team’s culture, and help shape the future of AI at Meta. Our internships are twelve (12) to sixteen (16), or twenty-four (24) weeks long and we have various start dates throughout the year.
Job Responsibility:
Develop novel SOTA generative AI algorithms and systems, leveraging deep learning techniques
Analyze and improve efficiency, scalability, and stability of deployed algorithms
Advance the science and technology of intelligent machines through research
Enable learning the semantics and training generative models of data (images, video, 3D, text, audio, and other modalities)
Disseminate research results and publish in top tier conference
Requirements:
Currently pursuing a Ph.D. in Computer Science, Artificial Intelligence, Generative AI, or a relevant technical field
Experience with Python, C++, C, Java, or related languages
Must obtain work authorization in the country of employment at the time of hire and maintain ongoing work authorization during employment
Experience building systems based on machine learning and/or deep learning methods
Nice to have:
Intent to return to the degree program after the completion of the internship/co-op
Proven track record of achieving significant results (grants, fellowships, patents, first-authored publications at leading workshops/conferences: NeurIPS, ICML, ICLR, ACL, EMNLP, CVPR, ICCV, ECCV, etc.)
Experience working and communicating cross-functionally in a team environment
Experience advancing AI techniques, including contributions to open source libraries/frameworks in computer vision or related fields
Publications or experience in machine learning, AI, computer vision, 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 utilizing theoretical and empirical research to solve problems