What are the responsibilities and job description for the Adjunct Instructor: Applications of NL(X) and LLM position at Carnegie Mellon University?
Carnegie Mellon University: Heinz College
Location
Open Date
Jul 23, 2026
Description
Adjunct Instructor: Applications of NL(X) and LLM
The Heinz College of Information Systems and Public Policy at Carnegie Mellon University seeks an adjunct instructor for Applications of NL(X) and LLM this fall semester. We invite professionals with deep experience and demonstrated leadership in the field to apply. This course is designed to provide graduate-level students with a comprehensive understanding of NL(X) and LLMs, focusing on their applications, evaluation, and operationalization across many industries. Beginning with the fundamentals of NL(X), the course covers its history, evolution, and critical applications, offering students hands-on experience with essential tools such as text mining, sentiment analysis, and embeddings.
As students’ progress, they will delve into advanced architectures, including RNNs, LSTMs, and Transformers, learning how these models drive key applications like machine translation and named entity recognition (NER). The course places significant emphasis on Large Language Models, such as GPT and BERT, guiding students through the intricacies of training, fine-tuning, and deploying these models. Advanced topics like Retrieval-Augmented Generation (RAG) and agentic architectures will also be explored, highlighting how LLM-based agents are transforming tasks that require complex reasoning, planning, and execution.
The course is an elective for students across Heinz College programs. Students are expected to have some background in artificial intelligence, and be able to program in Python at a basic level for assignments.
This is a mini (7 week long) course in the fall 2026 semester, which begins on Monday, August 24, 2026 and the last day of mini 1 classes is scheduled for Friday, October 9, 2026. The course will meet in-person twice weekly for 80 minutes, on Tuesdays and Thursdays at 5:00pm - 6:20pm. This course also offers an optional Friday recitation during the 7 week course, which meets 2:00pm - 3:20pm.
The course design should at minimum include relevant readings (textbook, research papers, news articles, etc.), in-class discussions, and appropriate evaluations of mastery of concepts for grading purposes (homework, quizzes/exams, etc.). Given the focus of Heinz College graduate programs, utilization of data, strategic thinking, and application of leadership skills are highly encouraged to be integrated into the course.
About Heinz College
The Heinz College of Information Systems and Public Policy is home to two internationally recognized schools: the School of Information Systems and Management and the School of Public Policy and Management. The unique colocation of these two schools sets Heinz College apart to tackle society’s most complex problems by teaching our students a firm understanding of policy, technology and analytical foundations, and the management skills to deploy solutions for maximum impact – the intersection of people, policy, and technology to approach complex problems. For more information, please visit www.heinz.cmu.edu.
Qualifications
The instructor should be a practitioner with direct experience in the field and should utilize data-driven analysis and methodology in their teaching. Recent experience in teaching is preferred.
Application Process
Equal Employment Opportunity Statement
Carnegie Mellon University is an equal opportunity employer. It does not discriminate in admission, employment, or administration of its programs or activities on the basis of race, color, national origin, sex, disability, age, sexual orientation, gender identity, pregnancy or related condition, family status, marital status, parental status, religion, ancestry, veteran status, or genetic information. Furthermore, Carnegie Mellon University does not discriminate and is required not to discriminate in violation of federal, state, or local laws or executive orders.
Benefits:
Health Insurance, Vacation & Paid Time Off, Professional Development