About the job
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.
Responsibilities
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.
Qualifications
Minimum
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.
Preferred
No preferred qualifications listed.