Research Scientist Manager, Generative AI - LLama Responsibilities:

• Deliver massive-scale datasets for training the next generation of LLama models
• Drive execution of NLP developing datasets end-to-end, including sourcing, curation, quality filtering, measurement, synthetic data, experiment design, across all sources of knowledge, and more
• Grow a team of domain experts within Language Data Foundation of Generative AI
• Communicate, collaborate, and build relationships with clients and peer teams to facilitate cross-functional projects
• Remain up-to-date on ongoing research and software development activities in the team, help work through technical challenges, and be involved in design decisions
• Remain deeply involved in the research community, both understanding trends, and setting them

Minimum Qualifications:

• 5 years of hands on experience in large language model, NLP, and Transformer modeling, in the setting of both research and engineering development
• Experience and track of recording in landing large research and/or product impacts in a fast-paced environment
• 3 years of hands on supporting and leading teams of research scientists and software engineers
• Proven technical vision in where the field of generative AI will go
• Experience in developing large-scale AI datasets, models, and/or services
• Experience with cross functional collaboration with product and platform teams, as well as non-engineering functions
• Demonstrated experience recruiting, building, structuring, leading technical organizations, including performance management

Preferred Qualifications:

• PhD in deep learning, artificial intelligence, and/or related technical field
• Experience building massive-scale datasets to train NLP and/or multimodal models
• Experience and knowledge of ML frameworks like PyTorch, TensorFlow, etc.
• Experience and knowledge of large-scale data platforms such as Spark, Hive, etc.
• Experience and knowledge of working with LLM frameworks like LangChain
• Experience and knowledge of training LLMs, fine-tuning on datasets, especially LLaMa

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