- Perform research to advance the science and technology of Llama multimodal training data and modeling recipes
- Develop novel state-of-the-art multimodal generative AI data, algorithms and corresponding systems, leveraging various deep learning techniques
- Collaborate with researchers and cross-functional partners including communicating research plans, progress, and results
- Disseminate research results
- Publish research results and contribute to research that can be applied to Llama models and Meta product development
- Currently has or is in the process of obtaining a Ph.D. degree in Computer Science, Computer Vision, Artificial Intelligence, Generative AI, or relevant technical field
- Must obtain work authorization in the country of employment at the time of hire and maintain ongoing work authorization during employment
- Research experience in machine learning, deep learning, computer vision and/or natural language processing
- 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 CVPR, ECCV, ICCV, ICLR, ICML, NeurIPS, AAAI, RecSys, KDD, IJCAI, ACL, NAACL, EACL, ICASSP, or similar
- Experience working and communicating cross functionally in a team environment
- 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 in utilizing theoretical and empirical research to solve problems
- Demonstrated software engineer experience via an internship, work experience, coding competitions, or widely used contributions in open source repositories (e.g. GitHub)
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