Job Description

Job Summary
The AI Engineers Program is a core component of Utah RAISE (Research & AI Infrastructure for a Statewide Ecosystem) and the broader University of Utah AI ecosystem. The program provides applied AI engineering expertise to complement the shared computing infrastructure to enable researchers, educators, and administrators at the U to adopt responsible and scalable AI adoption.

We are hiring a cohort of AI Engineers who can support design, development, and deployment of prototype to production-grade AI solutions for well-scoped and high-impact use cases. This cohort will focus on application-level work: model fine-tuning and adaptation, retrieval-augmented generation pipelines, agentic systems, and production-oriented ML engineering, and will help build durable, in‑house capacity to support teaching, research, and innovation across the university.

The AI Engineers Program will be administered by the SCI Institute in coordination with the Office of Artificial Intelligence and the One-U Responsible AI Initiative to serve the broader University of Utah community and beyond.

Responsibilities
Accelerate responsible AI integration across teaching and research, helping faculty and students move from ideas to production ready tools and prototypes.
Support rapid experimentation, prototyping, and implementation, which is increasingly critical given how quickly AI technologies are evolving.
Build and maintain shared tools, frameworks, and pipelines that can be reused across colleges and initiatives, reducing duplication and dependence on external vendors.
Help operationalize governance, privacy, and security expectations by embedding them directly into AI solutions.
Act as force multipliers, upskilling internal teams and supporting interdisciplinary collaboration.
Collaborate with faculty, researchers, and other institutional partners to scope projects and define technical approaches.
Provide mentorship and technical guidance to senior undergraduate and MS students in AI technologies and application development.
Contribute to open-source software and promote reproducible research practices, helping to foster a culture of transparency and reuse across campus.
Design and deliver workshops, training sessions, and educational materials on AI tools and methods.

Provide technical consulting to the campus community through structured office hours and project-based engagements.
Minimum Qualifications
Requires a bachelor’s (or equivalency) + 6 years or a master’s (or equivalency) + 4 years of directly related work experience. Assumes work equivalency (1 year of higher education can be substituted for 1 year of directly related work experience).
Professional experience working in technical roles involving complex software‑based systems, research computing environments, or data‑driven applications, with responsibilities beyond routine execution.
Evidence of applying advanced technical concepts independently, including systems design, problem decomposition, and implementation choices, in environments with incomplete or evolving requirements.
Demonstrated experience collaborating with non‑peer stakeholders (e.g., researchers, faculty, clients, domain experts, or organizational partners) to translate needs or ideas into technical work.
Professional experience operating in environments requiring judgment, discretion, and ethical or policy awareness, such as academic, research, healthcare, government, regulated industry, or similarly constrained settings.
Prior experience guiding or supporting others’ technical work, such as mentoring, advising, leading technical components of projects, or providing consultative support.

Demonstrated ability to communicate technical work clearly in written and verbal form to audiences with varying levels of technical expertise.
Preferences
Demonstrated ability to effectively collaborate with researchers and stakeholders across disciplines and levels of technical expertise.
Experience in one or more relevant technical domains supporting applied AI development, such as machine learning systems engineering, LLM/GenAI application development, software engineering (e.g., Python, C++, JavaScript), cloud computing, distributed systems, or full-stack development
Broad familiarity with AI and research computing and data practices, with the ability to apply sound technical judgment to design, build, and deploy projects with a high degree of independence and creativity.
Ability to identify collaboration opportunities and initiatives that would benefit from applied AI integration and partnership with SCI and related programs.
Demonstrated experience developing and applying AI/ML techniques (e.g., model training and fine-tuning, large language models, retrieval-augmented generation, or scalable ML systems) in research, scientific, or production-oriented contexts.