Research Software Engineer I/II/III

Classification Title:

Research Software Engineer I/II/III

Classification Minimum Requirements:

The position title and level will be commensurate with experience.

Level I - A Bachelor’s Degree in computer or physical science, statistics, bioinformatics, analytics, or similar field and two years of experience; Master’s Degree in computer or physical science, statistics, bioinformatics, analytics, or similar field.

Level II – A Bachelor’s Degree in computer or physical science, statistics, bioinformatics, analytics, or similar field and three years of experience; Master’s Degree in computer or physical science, statistics, bioinformatics, analytics, or similar field and one year of experience; Doctoral Degree in computer or physical science, statistics, bioinformatics, analytics, or similar field.

Level III - A Bachelor’s Degree in computer or physical science, statistics, bioinformatics, analytics, or similar field and five years of experience; Master’s Degree in computer or physical science, statistics, bioinformatics, analytics, or similar field and three years of experience; Doctoral Degree in computer or physical science, statistics, bioinformatics, analytics, or similar field and one year of experience.

Job Description:

This position will support the AI-Powered Athletics project, which is a collaborative effort between the University of Florida (UF) and the University Athletics Association (UAA). A cornerstone of the project is an on-prem database that utilizes HiPerGator, UF’s AI supercomputer. The database integrates structured and unstructured data from UF student-athletes on health, nutrition, academic performance, and sports performance, including wearable sensors at practices and games. The role is to build and maintain the database, including monitoring data ingestion, managing data pipelines, integrating new technologies and data types, managing role-based access control, ensuring industry standard documentation practices, and implementing best practices to ensure stability, security, and sustainability.  Additionally, this role offers opportunities to collaborate on front‑end development, such as creating dashboards and user interfaces for coaching staff and researchers to access, visualize, and interpret the data. Ability to interact with faculty and staff from Engineering, Research Computing, UF IT, Athletics, and other areas is essential.

This dynamic position will be integrated into the AI-Powered Athletics team, which is led by Dr. Jennifer Nichols (Biomedical Engineering) and Spencer Thomas (University Athletics Association). Our mission is to contribute to the research, education, and athletic communities at UF with innovation in athletic performance, AI, and technology.

Candidates will be expected to adhere to privacy regulations related to student-athletes, including but not limited to HIPAA, FERPA, NCAA compliance, and UAA reporting guidelines. Excellent communication skills and the ability to work on interdisciplinary teams is required.

Learn more about the AI-Powered Athletics project at https://aipoweredathletics.eng.ufl.edu/
Learn more about HiPerGator at https://ai.ufl.edu/research/hipergator/

• Maintain and scale the UF Athletics Databank
  • Manage ingestion and storage of multi-modal structured and unstructured athlete data
  • Integrate new data types and new technologies following good software design principles
  • Build automated systems for ensuring data quality
  • Implement and improve workflow orchestration to ensure data pipeline efficiency
  • Provision, monitor, and troubleshoot the database infrastructure
  • Create and maintain comprehensive software documentation
  • Conduct through testing of the databank and workflow to ensure system reliability
• Collaborate on athletic, research, and education projects
  • Collaborate with UF researchers, front-end developers, and athletics stakeholders to translate needs into technical requirements and features
  • Implement schema changes, feature flags, and API contracts to ensure smooth, reproducible data workflow
• Ensure security, compliance, and sustainability
  • Enforce data‑governance and security best practices: implement role‑based access controls, encrypt data at rest and in transit, and ensure compliance with HIPAA, FERPA, NCAA reporting, and UAA guidelines.
  • Identify and communicate areas for improvement and continued development
  • Design and implement audit processes.
• Continual improvement and professional development
  • Stay abreast of advancements in relevant technologies