Full Time Adjunct Faculty, MS Applied Artificial Intelligence

Full Time Adjunct Faculty, MS Applied Artificial Intelligence

Posting # 5535
• Position Status:
Faculty, Full-time temporary
•

Location:

San Diego
• Position Type:
Faculty, Shiley-Marcos School of Engineering

The MS-AAI program is one of the few graduate programs in the nation that emphasizes applied, real‑world artificial intelligence, integrating technical depth with ethics, moral responsibility, and AI for social good. Candidates with experience across all areas of artificial intelligence and machine learning will be considered. Preference will be given to candidates with the ability to develop and teach courses in deep learning, computer vision, large language models (LLMs), and agentic AI systems.

Applicants with strong industry experience, applied research experience, or prior teaching experience in higher education are especially encouraged to apply.

The Shiley-Marcos School of Engineering at the University of San Diego invites applications for a full‑time adjunct faculty in the Master of Science in Applied Artificial Intelligence (MS-AAI) program, with an appointment beginning in the Spring 2027 semester (January 2, 2027). This is an on‑campus position requiring the selected candidate to reside in or relocate to the San Diego area. The MS-AAI program is delivered in accelerated 7‑week course blocks, and all instruction occurs in person at USD.

This is a full‑time temporary, benefit‑based position with an anticipated end date of August 31, 2027. The appointment is renewable at the discretion of the University and dependent upon performance and continued funding.

• Demonstrate a strong commitment to teaching excellence in an applied graduate program.
• Deliver engaging instruction in 7‑week, in‑person course formats.
• Provide timely, constructive feedback and meaningful engagement with graduate students.
• Integrate industry experience, real‑world case studies, and emerging AI practices into the teaching curriculum.
• Mentor and advise graduate students on academic progress, career planning, and capstone project development.
• Uphold USD’s Vision, Mission, and Core Values, including academic excellence, community, ethical conduct, and compassionate service.
• Maintain high standards of academic integrity and student performance.
• Collaborate with the Academic Program Director and Program Coordinator on course development and continuous improvement.

Participate in departmental and university service and marketing activities as appropriate.

Candidates must be authorized to work in the United States.

• Master’s degree or higher in Artificial Intelligence, Computer Science, Data Science, Statistics, Electrical and Computer Engineering, or a closely related quantitative field.
• Demonstrated industry experience related to AI or applied research experience in artificial intelligence or machine learning.
• Strong foundations in machine learning principles, probability, statistics, and linear algebra relevant to deep learning and large language models.
• Strong interpersonal, collaborative, and professional communication skills.
• Commitment to equity, inclusion, student engagement, and inclusive teaching practices.

• Prior university‑level teaching experience, ideally in in‑person, project‑based, or experiential learning environments.
• Industrial experience in AI engineering, ML engineering, deep learning, generative AI, LLM development, computer vision, or MLOps/model deployment.
• Proficiency in developing and deploying modern AI/ML systems, including core languages (e.g., Python) and frameworks such as PyTorch, Tensor Flow, Scikit‑learn, Lang Chain, AWS Sage Maker, and vector databases.
• Ability to integrate ethical frameworks, responsible AI principles, and real‑world case studies into teaching.
• Experience mentoring graduate students or supervising capstone projects.
• Teaching responsibilities include lecturing, designing assignments and projects, grading, providing feedback, and supporting students academically and professionally.

Background check:
Successful completion of a pre‑employment background check and drug…