Research Scientist, Llama Applied LLM (Post-Training)
Meta

Llama large language models have been widely adopted in open source and are used to power many generative AI applications at Meta and beyond.Meta is seeking Research Interns to join our Llama post-training language team. 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 generative modeling, deep learning, natural language processing, machine learning, and reinforcement learning. Our interns have an opportunity to make core algorithmic advances and apply their ideas at an unprecedented scale.Our internships are twelve (12) to sixteen (16), or twenty-four (24) weeks long and we have various start dates throughout the year.

Research Scientist, Llama Applied LLM (Post-Training) Responsibilities:

• Develop state-of-the-art generative AI algorithms and data, to help more effectively align our LLMs during post-training stages.
• Improve the performance of our foundational Llama models, which have wide open source adoption.
• Based on the project, help analyze and improve efficiency, scalability, and stability of corresponding deployed algorithms.
• Collaborate with researchers and cross-functional partners including communicating research plans, progress, and results.
• Disseminate research results.
• When applicable, contribute to research that can be applied to Meta product development.

Minimum Qualifications:

• Must obtain work authorization in the 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 building systems based on machine learning and/or deep learning methods.

Preferred Qualifications:

• 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 AI techniques, including core contributions to open source libraries and frameworks in computer vision.
• 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 training and aligning LLMs.

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