- Develop novel state-of-the-art retrieval augmented generation (RAG) algorithms and corresponding systems, leveraging various deep learning techniques
- Based on the project, help analyze and improve efficiency, scalability, and stability of corresponding deployed algorithms
- Perform research to advance the science and technology of intelligent machines
- Perform research that enables learning the semantics of data (images, video, text, audio, and other modalities)
- 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 Ph.D. degree in Machine Learning or Artificial Intelligence, 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 or other related language
- Experience building systems based on machine learning and/or deep learning methods
- Intent to return to 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, CVPR, ICCV, ECCV, ACL, EMNLP or similar
- Experience working and communicating cross functionally in a team environment
- Experience in advancing AI techniques in retrieval augmented generation (RAG), large language model (LLM), including core contributions to open source libraries and frameworks in RAG
- Experience solving analytical problems using quantitative approaches
- Experience setting up ML experiments and analyze 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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