Job description:

Most of the world's data+AI problems lie in enterprise domains, behind closed doors. Our research team's goal is to push the frontier of "domain adaptation" - how can we develop LLMs and AI systems that work well for custom domains. To do this we are tackling open research problems on a range of topics, from how to scale/automate eval, fine tune with synthetic data, retrieval augmentation, fast/efficient inference and more.

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You will work with our research team on projects focused on adapting LLMs and AI systems towards enterprise domains. This may include:

• Adapting, improving, and evaluating a method from the literature.
• Designing an entirely new method for domain adaptation.
• Composing together multiple methods to create new recipes for efficient post-training.
• Evaluation of LLMs and AI systems.

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Your qualifications and qualities:

• Required:
  • Research experience in and proficiency with the fundamentals of deep learning.
  • Pursuing a PhD in computer science or related fields (electrical engineering, neuroscience, physics, math, etc.).
  • Proficient software engineering skills, including with PyTorch.

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