Bioinformatics Programmer at UCSF Health in San Francisco, CA

Summary: UCSF Health is a leading university dedicated to promoting health worldwide through advanced biomedical research and education. They are seeking a Bioinformatics Programmer to apply machine learning and AI concepts for analyzing large-scale biological data and to develop AI agents and pipelines for biological discovery. Responsibilities: - Apply machine learning and artificial intelligence (AI) concepts for the analysis of large scale genomics, single cell, and other biological data - Responsible for the development and implementation of AI agents and pipelines as innovation tools for biological discovery - Follow standard programming procedures for documentation and data analysis Required Qualifications: - Bachelor's degree in biological sciences, computational biology, bioinformatics, engineering or related area and / or equivalent experience / training - Basic knowledge in programming languages like Python and common ML frameworks (PyTorch, TensorFlow, Huggingface etc.) - Basic knowledge in applying AI/ML approaches to biological data such as sequences, single cell or bulk genomics, multi-omics, spatial and imaging or other biological datasets - Basic knowledge experience in state-of-the-art Generative AI application development, LLM model tuning, agentic AI, and model context protocol (MCP) Preferred Qualifications: - Interpersonal skills in order to work with both technical and non-technical personnel at various levels in the organization - Ability to communicate technical information in a clear and concise manner - Self motivated, able to learn quickly, meet deadlines and demonstrate problem solving skills - Basic knowledge of bioinformatics methods for genomic data processing and analysis - Basic knowledge of modern biology and applicable field of research Required Skills: Python, Machine learning frameworks, PyTorch, TensorFlow, Huggingface, Artificial intelligence, Generative AI, Large language model tuning, Agentic AI, Model context protocol, Bioinformatics methods, Genomic data analysis, Computational biology, Biological data analysis