Applications Programmer

Job Description This is a Contract position with a 3-month end date from the start date. Job Function Summary Engaged in the development and automation of AI‑driven regulatory science systems and population pharmacokinetics modeling workflows. Responsibilities include assisting with constructing large language models (LLMs) using Retrieval‑Augmented Generation (RAG) and benchmarking their performance against leading models such as ChatGPT and Gemini. Support in designing, implementing, and maintaining automated pipelines for model construction, optimization, and result reporting. Additionally, responsible for assisting with developing and managing data sharing and archiving systems to support research projects, ensuring data integrity, accessibility, and reproducibility. Work supports full‑cycle system design, implementation, evaluation, maintenance, and documentation. Scope Entry‑level professional with limited prior experience; learns to use professional concepts to resolve problems of limited scope and complexity; works on assignments that are initially routine in nature, requiring limited judgment and decision making. Employees at this level are expected to acquire the skills and knowledge to perform more advanced work following an agreed‑upon time in position, through defined training and development planning. Under general supervision, applies professional computational and data science concepts to the development and automation of AI‑driven modeling systems in regulatory science and pharmacokinetics. Assists with projects of moderate scope involving large language model (LLM) construction using RAG frameworks, model performance benchmarking, and workflow automation for population pharmacokinetics modeling. Follows established research and development procedures to collect, analyze, and manage scientific data. Contributes to the design and maintenance of data sharing and archiving systems to ensure research reproducibility and compliance with institutional policies. Responsibilities Assist with constructing a large language model of regulatory science using RAG and compare in‑house model performance with benchmark LLMs such as ChatGPT and Gemini. Assist with automating the model construction, optimization and result reporting of population pharmacokinetics modeling. Support design and maintenance of a data sharing and archiving system for research projects. Qualifications Bachelor's degree in related area and/or equivalent experience / training. Skill in programming languages commonly used in AI and data science (e.g., Python, R) and in the use of machine learning libraries and APIs. Demonstrated effective communication and interpersonal skills. Demonstrated ability to communicate technical information to technical and non‑technical personnel at various levels in the organization. Self‑motivated and works independently and as part of a team. Able to learn effectively and meet deadlines. #J-18808-Ljbffr University of California, San Francisco