Bioinformatics Programmer 5
- Compensation
- Not disclosed
- Location
- San Francisco, CA, United States
- Work setup
- Not specified
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- Not specified
- Category
- Not specified
- Posted
Skills
Must have
- Bachelor's degree in biological science, computational / programming, or related area and / or equivalent experience / training.
- Expert knowledge of structural bioinformatics and protein-protein interaction analysis, including the computational methods and data structures used to represent protein structures and interaction net
- Minimum of ten years of experience designing and implementing computational methods for biological research, with expert-level proficiency in Python for scientific computing.
- Expert knowledge of AI/ML methods applied to biology, including deep-learning protein structure prediction and applied use of LLM-based tools.
- Expert knowledge of modern biology and the applicable field of research, including structural biology, quantitative proteomics, functional genomics, and their underlying experimental methods.
- Demonstrated ability to work with experimental researchers to interpret experimental readouts, translate computational predictions into testable hypotheses, and contribute to experimental design.
- Demonstrated ability to provide computational leadership across multi-year, multi-investigator research programs involving multiple UCSF units and external collaborating institutions.
- Highly developed interpersonal skills and ability to communicate technical information clearly to technical and non-technical personnel at all levels internally and externally.
- Highly developed ability to interface with management on a regular basis.
- Demonstrated ability to provide authoritative technical consultation to senior scientific investigators and research leaders on complex computational strategy, methodology, and interpretation.
- Working knowledge of computational infrastructure and reproducible development practices, including cloud platforms, containerization, and version control.
- Working knowledge of research data governance, data security, and scientific data management practices.
Nice to have
- Ph.D. in biophysics, computational biology, bioinformatics, structural biology, or a related field.
- Demonstrated external recognition as a subject matter expert, such as first-author publications in leading journals and invited presentations at international scientific meetings.
- Experience designing, building, and evaluating LLM-based agents and agentic workflows for scientific applications, including retrieval-augmented analysis and systematic evaluation.
- Experience applying computational methods in drug discovery or translational research, including target identification and mechanism-of-action analysis.
- Experience mentoring or providing technical direction to scientific staff, postdoctoral fellows, and students.
- Experience with graph neural networks or other deep-learning approaches applied to biological networks
Full description
Job Summary
The Quantitative Biosciences Institute (QBI) at UCSF brings together researchers across the biomedical, physical, and computational sciences to address complex problems in biology and human disease. QBI supports large, multi-investigator programs that integrate quantitative proteomics, structural biology, genetics, and advanced computational and AI methods, and works extensively with academic, government, and industry partners in the United States and internationally. Based at Byers Hall on the UCSF Mission Bay campus, QBI provides a highly collaborative, interdisciplinary environment in which computational scientists work directly alongside experimental groups to translate large-scale biological data into mechanistic understanding and therapeutic hypotheses.
Custom Scope
Functions as a recognized expert in computational bioinformatics, AI/machine learning, and structural biology, with responsibility for developing and directing advanced computational approaches used across multiple major QBI research programs and collaborations. Applies expert knowledge to highly complex, novel problems in protein-protein interaction prediction, structural modeling, scientific AI, and large-scale biological data integration where established approaches may be inadequate or nonexistent.
Provides computational leadership across multi-investigator research programs involving researchers from multiple UCSF schools, departments, and research centers, as well as domestic and international academic institutions and other external collaborators. Current work supports major QBI collaborative initiatives, including the ARIA-funded autism program and research collaborations involving institutions such as Gladstone Institutes, NYU Langone Health, Johns Hopkins University, and the Icahn School of Medicine at Mount Sinai, as well as research supported through federal programs including NIH and DARPA.
Serves as a senior technical authority who shapes computational strategy, establishes and advances technical standards and reproducibility practices, develops computational methods and resources intended for reuse across research teams, and provides authoritative consultation to scientists and computational professionals. Represents QBI and UCSF computational expertise through collaborative publications, presentations, methods, and resources shared internally and externally. Provides technical direction and mentorship to computational staff, postdoctoral researchers, and students.
%
of timeEssential Function (Yes/No)
Key Responsibilities(To be completed by Supervisor)
25
YesApplies advanced bioinformatics concepts to design, develop, modify, debug, and evaluate the most complex software programs, systems, and web tools.
- Leads the design, development, evaluation, and optimization of highly complex computational methods for protein-protein interaction prediction, structural characterization, and biological data integration. Establishes computational approaches for novel research questions where established methods are insufficient, and determines methodological strategies for high-impact collaborative research programs.