Graduate Intern - LLM Reliability and Uncertainty for AI Science Assistants

What you'd do

• Research and evaluate uncertainty quantification and hallucination detection methods for multi-turn, agentic scientific workflows
• Develop probing methods that predict, from a model's internal representations, when a scientific task specification is incomplete or inconsistent and a clarifying question is warranted
• Build and instrument evaluation pipelines that capture and analyze model internal states over multi-turn scientific dialogue on HPC systems
• Conduct experiments and analyze model behavior across computational science domains and established benchmarks
• Contribute to technical documentation, research reports, publications, and presentations summarizing project progress and findings
• Develop, test, and maintain high-quality research code and evaluation pipelines

What they want

• Applicants are responsible for uploading official or unofficial school transcripts, as part of the application process.
• If selected for position, a letter of recommendation will be required as part of the hiring process.
• Must meet educational requirements prior to employment start date.
• Familiarity with large language models, including agentic, tool-using, or multi-turn conversational LLM systems
• Experience developing or evaluating machine learning models for classification, uncertainty estimation, or related tasks
• Knowledge of probabilistic machine learning or uncertainty quantification concepts
• Hands-on experience with open-weight LLMs and modern deep learning frameworks
• Experience running Python code on HPC or multi-GPU systems
• Strong software engineering and debugging skills
• Ability to work independently while collaborating effectively in a multidisciplinary research environment