Research Software and AI Engineer
About The Position
This position is part of the Data Science and AI Research Service team which reports to the Director of Cross-Sector Partnerships. The Data Science and AI Research Service is a team within the School of Data Science and Society that supports data-intensive research across a broad range of data science and AI domains. The service works to enable best practices in data management, analysis, software development, and technology adoption to advance data science research and research translation. The Research Software and AI Engineer is a team member within the Data Science and AI Research Service that supports researchers in developing and deploying AI and data science applications. The role works closely with faculty, research staff, and graduate students to enable cutting-edge data science research. The role designs, builds, and hardens software tools. The role collaborates with multi-disciplinary teams and external partners to deliver production-quality code, taking prototypes to scalable, maintainable tools. Where appropriate, the Research Software and AI Engineer incorporates state-of-the art technologies and modern development practices to maximize efficiency and usability for end users.
Requirements
- Bachelor’s degree in Computer Science, Software Engineering, or a relatied field; or an equivalent combination of education and/or experience.
Nice To Haves
- Demonstrated professional experience in software engineering and web development, including Python, SQL , modern development frameworks, Git workflows, automated testing, CI/CD pipelines, and containerization.
- Experience delivering production services from prototypes in iterative sprints.
- Experience with modern frameworks such as React, Django, FastAPI is a plus.
- Experience with front-end development is a plus.
- Familiarity with accelerators, software templates, and reference architectures.
- Experience with AI and Machine Learning Frameworks, including Generative AI
- Exposure to Azure ecosystems (e.g., Microsoft Fabric, Azure MI, Databricks) and collaboration with data/ML and cloud teams.
- Familiarity with reasearch data contexts (e.g., working with curated datasets, data catalogs, access controls) and secure, HIPAA protected environments.
- Familiarity with HPC cluster workflows, including SLURM job scheduling, resource allocation, and parallel computing.
