Open Source LLM Clinical Research Pipeline Master's Intern

2026 Summer Internship

FLEXIBLE: This internship role is scheduled to be flexible. Please be prepared to spend part of your time working remotely and part of your time working from the following location: 100 S. Los Robles Ave., Pasadena, 91101.

Job Summary:

Note: THIS POSITION MUST BE REVIEWED WITH TOTAL COMPENSATION BEFORE POSTING. This position provides analytical support to a project or department. Under the guidance of a Project Manager/ Director, responsible for collecting and researching data, performing analysis, providing recommendations and presenting results.

Department Description:

The Department of Research & Evaluation has an integral role in the success of Kaiser Permanente Southern California by conducting high-quality, innovative translational research that benefits the health of its members and the communities from which they come. Our mission is to initiate and conduct high-quality, public-sector health services, epidemiologic, behavioral, and clinical research that has a demonstrable positive impact on the health and well-being of Kaiser Permanente Southern California members and the general population.

Project Description:

This project will explore/deploy the current available and common open-source LLM models which can be effectively leveraged to support research studies in the research and evaluation department. Specific tasks includes but not limited to 1) Explore/identify open-source LLM models frequently used for clinical research studies; 2) Download/test the identified  LLMs in local secured computing environments managed by REIT; 3) Build a standardized python package/pipeline can perform training/fine-tuning/inference tasks for disease classification/predication, clinical information retrieval, clinical text summarization/standardization,  evaluation of LLM-generated summarization, etc. 4) Evaluate/test  the built pipeline for one disease area, such as aortic stenosis in  cardiovascular disease.      

Target Majors:

MS or MPH degree in Biostatistics or Computer Science/Data Science.

Desired Skillsets:

Recent Computer Science/Data Science Masters Graduate or PhD Candidate.
Proficient Python programming skills.
Strong analytical thinking and problem-solving skills.
Experience with LLM.
Experience with or knowledge of electronic medical records.
Computer Science/Data Science major preferred.

Essential Responsibilities:

• Responsible for contributing high level analytical support to a project or department requiring application of knowledge in marketing, information management, finance, management, economics or statistics.
• This position contributes to a full range of project components.
• Assigned to at least one project and expected to produce results and report out as a member of a project team.
• Organizes assigned project tasks, identifies and structures information collection activities, and sets timelines for completion of required tasks as directed.
• Develops tools to identify and to document best practices.
• Prepares various excel, word or PowerPoint reports, and works with access database software (or similar tool) to develop data extracts and management information reports.
• May be asked to produce graphic information using excel or PowerPoint or similar software.
• Contributes to development of management information and analysis conducted, provides recommendations and assists with preparation of formal presentations to communicate results to various management levels.  Identifies alternative solutions to project issues.
• Contributes to development project plans to ensure timely deliverables and successful completion of project and demonstrates an understanding of various business intersections.
• Assists with identification and development of training information.
• Kaiser Permanente conducts compensation reviews of positions on a routine basis. At any time, Kaiser Permanente reserves the right to re-evaluate and change job descriptions, or to change such positions from salaried to hourly pay status. Such changes are generally implemented only after notice is given to affected employees.