AI Studio Community & Research Lead

Job Posting Title:

AI Studio Community & Research Lead

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Hiring Department:

Enterprise Technology - Learning Technology

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Position Open To:

All Applicants

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Weekly Scheduled Hours:

40

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FLSA Status:

Exempt

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Earliest Start Date:

Immediately

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Position Duration:

Expected to Continue Until Apr 30, 2030

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Location:

AUSTIN, TX

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Job Details:

General Notes

This is a fixed term position that ends four years from the employee’s start date.

Flexible work arrangements are available for this position, including the ability to work remotely at least 50% of the time within the United States. In person meetings will be required when applicable. We prefer a candidate located in the greater Austin area as travel to campus for occasional in-person events, training, team meetings, activities, etc., will be required.

This position provides life/work balance with typically a 40-hour work week and travel limited to training (e.g., conferences/courses).

Enterprise Technology is dedicated to supporting the mission of the University of Texas at Austin of unlocking potential and preparing future leaders of the state.

Your skills will make a difference.

You’ll be working for a university that is internationally recognized for research and the work you do will make a difference in the lives of our students, faculty and staff. If you’re the type of person that wants to know your work has meaning and impact, you’ll like working for our campus.

The University of Texas at Austin and Enterprise Technology provide an outstanding benefits package to our staff. Those benefits include:

• Competitive health benefits (Employee premiums covered at 100%, family premiums at 50%)

• Vision, Dental, Life, and Disability insurance options

• Paid vacation, sick leave, and holidays

• Teachers Retirement System of Texas (a defined benefit retirement plan)

• Additional Voluntary Retirement Programs: Tax Sheltered Annuity 403(b) and a Deferred Compensation program 457(b)

• Flexible spending account options for medical and childcare expenses

• Training and conference opportunities

• Tuition assistance

• Athletic ticket discounts

• Access to UT Austin's libraries and museums

• Free rides on all UT Shuttle and Capital metro buses with staff ID card

For more details, please see: https://hr.utexas.edu/prospective/benefits and https://hr.utexas.edu/current/services/my-total-rewards

Must be authorized to work in the United States on a full-time basis for any employer without sponsorship.

This position requires you to maintain internet service and a mobile phone with voice and data plans to be used when required for work.

Purpose

Enterprise Technology’s AI Studio is the university’s hub for applied AI exploration, responsible adoption, and community building. The AI Studio Community & Research Lead is a faculty-facing, program-building role at the center of UT Austin’s effort to help the campus understand, adopt, and use AI thoughtfully. This position designs and runs the programs that connect faculty with AI tools, expertise, and each other through fellowships, events, workshops, and embedded research partnerships. The role serves as a natural bridge between the faculty research community and UT.AI Studio’s broader ecosystem, including the UT.AI Academy’s student talent development program, creating pathways that connect faculty AI engagement with the next generation of AI practitioners being trained on campus.

Responsibilities

Engagement & Research Support:

• Serve as the primary point of contact between the AI Studio and UT’s faculty community, building trust-based relationships across colleges and disciplines.
• Conduct ongoing faculty listening and needs assessment to shape Studio programming and communicate gaps back to ET leadership.
• Develop and maintain a network of faculty champions and early adopters who help pilot new tools and share outcomes with peers.
• Provide consultative support to faculty and research teams exploring AI applications in their scholarly work, including human-AI collaboration, archival research, data analysis, and computational approaches