Job Summary:
We are seeking a Principal Research Software Engineer to serve as the primary technical expert supporting a nationally recognized health policy research program.
Our program's active research collaborations span Harvard University, Mass General Brigham, Yale University, Penn State, the National Bureau of Economic Research (NBER), the Naval Postgraduate School (NPS), and public universities both within California and in other states, including UCLA, UC Davis, the University of Washington, the University of Texas, and the University of Florida. This position is responsible for ensuring that our data and technical platforms are usable, interoperable, and compliant across this entire range of environments — private research universities, public universities in and outside California, a major academic health system, a nonprofit economic research organization, and a federal Department of Defense institution — each with its own data governance, security, and regulatory regime, including federal information-security requirements on the NPS side.
Department Summary:
The Philip R. Lee Institute for Health Policy Studies (PRL-IHPS) is an organized research unit within the School of Medicine (SOM). The primary purposes of the Institute are to advance knowledge of health services and health policies through basic and applied research; to contribute to the solution of health and social problems through the application of research findings to health policy issues at the national, state, and local levels.
% of time | Essential Function (Yes/No) |
Key Responsibilities (To be completed by Supervisor) |
| 55 | Yes | Research Software Engineering and Data Platforms Design, develop, test, debug, document, and maintain research software systems, data architectures, metadata management capabilities, and analytical infrastructure that enable researchers to discover, understand, integrate, access, and analyze complex datasets efficiently, reproducibly, securely, and at scale. Modernize legacy systems and automate operational and research workflows to improve reliability, security, and efficiency across the program’s technology ecosystem. Serve as the primary technical expert for major public, administrative, clinical, policy, and observational datasets used by the research program, developing deep expertise in their structure, provenance, strengths, limitations, and appropriate analytical applications. Establish technical standards, architecture principles, engineering practices, code review and quality oversight processes, and governance approaches, adopted across the program and its collaborating institutions, that ensure the reliability, maintainability, reproducibility, and long-term sustainability of research technology assets. Holds overall responsibility for the quality of the program’s software and data systems and their integration with institutional and external systems. |
| 25 | Yes | AI Strategy and Enablement
Evaluate, prototype, and responsibly deploy emerging artificial intelligence technologies that enhance research productivity, scientific discovery, data exploration, and analytical workflows, including both locally hosted open-weight models and externally hosted commercial models, within environments containing sensitive or regulated data. This work has little or no precedent and requires formulating governance strategies and policies for the responsible use of AI with regulated health data, with significant influence on organizational policy and program development.
Develop AI-enabled tools and workflows supporting activities such as dataset exploration, variable discovery, cohort identification, literature synthesis, research workflow automation, natural language interaction with structured data, and research knowledge management.
Assess emerging AI, computational, and data technologies and identify opportunities where they can responsibly improve research quality, efficiency, reproducibility, and scientific impact.
Design and establish reusable technical capabilities that can support multiple research projects, datasets, investigators, and collaborating institutions.
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| 15 | Yes | Research Technology Leadership and Strategy
Develop and maintain, in partnership with the Principal Investigator and research leadership, a multi-year technology roadmap that aligns software, data, AI, and computational investments with evolving scientific priorities.
Evaluate emerging opportunities, prioritize and direct technical investments, and formulate research technology strategy, administering the policies, processes, and resources of the program’s technology portfolio, to maximize scientific impact and long-term organizational capabilities.
Partner closely with investigators, research staff, and external collaborators to understand scientific objectives, provide guidance on the effective use of software, data, AI, and emerging technologies, and translate research needs into scalable technical capabilities.
Provide technical leadership for grant proposals, publications, presentations, collaborative research initiatives, and infrastructure planning efforts, helping identify and pursue funding opportunities that expand the program’s technical, computational, data, and AI capabilities through technical proposal development and research infrastructure design.
Provide technical leadership and consultation on software architecture, data systems, artificial intelligence, and emerging computational methods. Direct and review the technical work of research staff, analysts, and other technical contributors within the program and across collaborating institutions; mentor technical staff and trainees; and promote software engineering best practices across collaborative projects.
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| 5 | Yes | Knowledge Sharing, Open Science, and Community Impact
Develop reusable software, data tools, standards, documentation, training materials, and educational resources, and contribute to shared research infrastructure, open-source initiatives, and open-science efforts that advance research collaboration and scientific impact, extending the program’s impact beyond the University to the broader research community.
Promote the responsible adoption of artificial intelligence, data, and computational methods through consultation, demonstrations, workshops, training, and collaborative engagement with researchers and research staff.
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| 100% |
Required:
Preferred:
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