Applied Machine Learning Intern Jobs in El Paso, TX

Department Contact: Enrico Pontelli, 575-646-2001, epontell@nmsu.edu
Internal or External Search: External - Open to all applicants
Advertising Summary: New Mexico State University invites applications for two tenure-track Assistant Professor positions in Applied AI (9-month appointments). Faculty will join an interdisciplinary cluster hire and be affiliated with the new Institute for Applied Practice in AI and Machine Learning.
Position Details
Position Title: Assistant Professor - Artificial Intelligence Education
College/Division: Arts and Sciences College
Department: 330500-COMPUTER SCIENCE
Location: Las Cruces
Offsite Location (if applicable):
Target Hourly/Salary Rate: Commensurate with qualification
Appointment Full-time Equivalency: 1.0
FLSA Status: Exempt
Bargaining Unit Announcement: This is a bargaining unit position with the National Education Association (NEA).
Contingent Upon Funding: Not Applicable
Standard Work Schedule: Standard (M-F, 8-5)
If Not a Standard Work Schedule:
Job Duties and Responsibilities: Faculty in this position are expected to:
• Conduct innovative, externally funded research on the teaching, learning and application of artificial intelligence, including AI pedagogy, curriculum design, credentialing, and the study of the most effective ways to teach AI to different audiences (students, professionals, educators, and community members).
• Teach undergraduate and graduate courses in artificial intelligence and its applications, contributing to AI curriculum development across disciplines
• Partner with domain specialists across colleges to design and build curricula that embed AI into disciplinary programs of study.
• Lead the development of microcredentials, certificates, and other flexible credentialing pathways in AI.
• Develop and deliver AI training and workforce development programs for diverse audiences within and beyond the university.
• Advise and mentor students at the undergraduate, master's, and doctoral levels.
• Engage in professional and institutional service, including outreach, committee participation, and strategic initiatives.
• Dedicate a portion of assigned effort to the educational activities of the NMSU Institute for Applied Practice in AI and Machine Learning, and promote NMSU's role as a leader in AI education for the state and region.
Qualifications
Required Education and Experience:Education: Earned Ph.D. in a discipline relevant to the position (e.g., artificial intelligence, computing or computational sciences, learning sciences, education, or a relevant AI applied field, e.g., agriculture or manufacturing) by the time of hire.
Experience:
• Demonstrated expertise as an AI generalist with a primary focus on teaching, AI curriculum development, credentialing, pedagogy, or educational integration of artificial intelligence.
• Evidence of scholarly productivity or high potential for conducting research in educational frameworks, curriculum design, or pedagogical methods related to AI.
• Experience or strong potential for securing external funding to support computing/AI education and program development.
• Demonstrated commitment to excellence in teaching, curriculum development, and student mentoring.
Additional Qualifications:
• Ability to work effectively in a collaborative, multidisciplinary academic environment.
• Preferred experience includes development of microcredentials or professional training programs, integration of AI into non-computing curricula, responsible AI practices, and support of diverse student populations.
Equivalent Qualifications:
Preferred Qualifications:Please submit the following documentation with the application:
-CV
-Statement of Teaching
-Statement of Research
Special Certification/Licensure:
Working Conditions and Physical Effort
Environment: Work is normally performed in a typical interior/office work environment.
Physical Effort: No or very limited physical effort required.
Lifting Requirements: Requires handling of average-weight objects up to 10 pounds or some standing or walking.
Risk: No or very limited exposure to physical risk.