Bioinformatics Programmer 5

UCSF•San Francisco, CA

1d•$156,135 - $378,945•Onsite

About The Position

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. 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.

Requirements

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 networks.
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 Haves

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

Responsibilities

Applies 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.
Conducts highly specialized bioinformatics programming and testing of new algorithms that will interact with other related databases, requiring extensive expertise in the field.
Leads development and scientific governance of LLM-based agents and agentic workflows for research applications, including tool and data-source integration, automated analysis, evaluation, grounding, provenance, and human-review processes. Establishes standards and practices for responsible and reproducible use of scientific AI methods across collaborating research teams.
Formulates extremely sensitive or complex logic for new systems, devises logic procedures, prepares flowcharting, performs coding and data analysis, and tests / debugs highly complex programs.
Develops computational methods, resources, pipelines, and reproducibility frameworks intended for reuse across multiple research teams and collaborations, and establishes technical standards for their implementation, validation, documentation, and maintenance.
Leads changes in development, maintenance and system standards for sequence analysis algorithms, tools, and infrastructure.
Provides computational leadership for cross-disciplinary research programs, translating biological questions into computational strategies, evaluating alternatives, and determining appropriate analytical approaches in collaboration with experimental investigators across UCSF and external institutions.
Performs extremely complex data modeling, performance and integration testing and builds screens for a variety of internal and external constituents with broad scope and impact.
Directs computational modeling and integration of large-scale proteomic, genetic, structural, and other biological datasets across multi-investigator programs to identify protein complexes, infer molecular architecture, and prioritize functional and therapeutic targets. Determines analytical strategies and resolves novel methodological challenges with broad implications for collaborative research programs.
Represents QBI/UCSF computational expertise externally through collaborative publications, invited presentations, scientific meetings, and dissemination of computational methods, datasets, and resources to domestic and international collaborators and the broader scientific community.
Serves as an authoritative computational consultant and subject matter expert to UCSF investigators, QBI research programs, and external collaborators; advises on complex methodological questions, interprets computational results, and helps translate findings into experimentally testable hypotheses.
Provides technical direction, mentorship, and expert guidance to computational staff, postdoctoral fellows, students, and collaborators working on related computational research activities.