University of California, San Francisco · San Francisco, CA
The Arnaout Lab at UCSF seeks an experienced Data Engineer to who wants to participate in cutting-edge research with transformational impact to clinical and research medicine across a wide array of diseases, working with decades of high-quality medical data alongside clinical domain experts. The successful candidate will use local, hybrid, and/or cloud computing to develop strategies and software/hardware pipelines for modular, secure automation, scaling, and crowdsourcing of data mining, preprocessing, storage, labeling and computing. The position also provides opportunities to publish, present at research conferences, and for professional advancement.
Develops and optimizes a variety of computational, data science, and CI research tools and components. Performs research on current and future high performance computing (HPC), data, and CI technologies, hardware and software projects. Works on algorithm development, optimization, programming, performance analysis and/or benchmarking assignments of moderate scope where the tasks involve knowledge of either domain/computer science research requirements and/or CI design/implementation requirements.
Please note: This is a full-time one-year contract position starting from the date of hire.
The Arnaout laboratory studies deep and machine learning for biomedical imaging and related clinical data, with the goals of decreasing diagnostic error and developing and scaling novel phenotypes to drive precision medicine research.
UCSF is a top-10 medical center and a leader in cross-campus efforts to mine, harmonize, and analyze multi-modal clinical data for the University of California’s 15 million patients.
The Arnaout laboratory is part of both the Bakar Computational Health Sciences Institute, where the abovementioned efforts are based, and the nationally ranked Department of Medicine. Projects focus on deep learning for medical imaging, and through collaborative work with intra- and inter-institutional partners, touch the electronic health record, genetics, and other sources of data.
• Bachelor's degree in Computer/Computational/Data Science, or Domain Sciences with computer/computational/data specialization and 3+ years of experience, or the equivalent combination of experience/education
• Intermediate knowledge of HPC/data science/CI
• Advanced skills, and demonstrated experience associated with one or more of the following: HPC hardware and software power and performance analysis and research, design, modification, Implementation and deployment of HPC or data science or CI applications and tools
• Proven skills and experience in independently resolving broad computing/data/CI problems using introductory and/or intermediate principles
• Proven ability to understand research computing/data/CI needs, mapping use cases to requirements and how systems/software/infrastructure can support those needs and meet the requirements. Demonstrated ability to develop and implement such solutions
• Demonstrated knowledge of database design and deployment on local, cloud, or hybrid platforms
• Demonstrated knowledge of containers (e.g. Docker, Kubernetes)
• Demonstrated ability to work with data in a responsible secure manner
• Demonstrated ability to design and deploy secure web applications
• Demonstrated ability to regularly interface with management
• Demonstrated effective communication and interpersonal skills. Demonstrated ability to communicate technical information to technical and non-technical personnel at various levels in the organization and to external research and education audiences
• Demonstrated experience and ability to collaborate effectively with all levels of staff; technical, students, faculty and administrators
• Self-motivated and works independently and as part of a team. Able to learn effectively and meet deadlines
• Proven ability to successfully work on multiple concurrent projects
• Master’s or PhD in Computer Science, Software Engineering, or related field
• Undergraduate-level background in biology, physiology and/or medicine
• Demonstrated ability to contribute research and technical content to grant proposals
• Thorough experience working in a complex computing/data/CI environment encompassing all or some of the following: HPC, data science infrastructure and tools/software, and diverse domain science application base
• Demonstrated broad experience in one or more of the following: optimizing, benchmarking, HPC performance and power modeling, analyzing hardware, software, and applications for HPC/data/CI
The University of California, San Francisco (UCSF) is a leading university dedicated to promoting health worldwide through advanced biomedical research, graduate-level education in the life sciences and health professions, and excellence in patient care. It is the only campus in the 10-campus UC system dedicated exclusively to the health sciences. We bring together the world’s leading experts in nearly every area of health. We are home to five Nobel laureates who have advanced the understanding of cancer, neurodegenerative diseases, aging and stem cells.
UCSF is a diverse community made of people with many skills and talents. We seek candidates whose work experience or community service has prepared them to contribute to our commitment to professionalism, respect, integrity, diversity and excellence – also known as our PRIDE values.
In addition to our PRIDE values, UCSF is committed to equity – both in how we deliver care as well as our workforce. We are committed to building a broadly diverse community, nurturing a culture that is welcoming and supportive, and engaging diverse ideas for the provision of culturally competent education, discovery, and patient care. Additional information about UCSF is available at diversity.ucsf.edu
Join us to find a rewarding career contributing to improving healthcare worldwide.
Equal Employment Opportunity
The University of California San Francisco is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, protected veteran or disabled status, or genetic information.