Adjunct Lecturer, Applied Deep Learning and AI (Online, Fall '26)
ColumbiaUniversity1
Posted: February 5, 2026
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Quick Summary
The Adjunct Lecturer, Applied Deep Learning and AI at Columbia University is responsible for teaching and mentoring students in the School of Professional Studies, with a focus on deep learning and AI. The ideal candidate will have a strong background in deep learning and AI, as well as excellent teaching and mentoring skills. The successful candidate will be expected to develop and deliver high-quality courses, lead student projects, and contribute to the development of the School's research programs.
Required Skills
Job Description
Columbia University has been a leader in higher education in the nation and around the world for more than 250 years. At the core of our wide range of academic inquiry is the commitment to attract and engage the best minds to pursue greater human understanding, pioneering discoveries, and service to society.
The School of Professional Studies at Columbia University offers innovative and rigorous programs that integrate knowledge across disciplinary boundaries, combine theory with practice, leverage the expertise of our students and faculty, and connect global constituencies. Through seventeen professional master's degrees, courses for advancement and graduate school preparation, certificate programs, summer courses, high school programs, and a program for learning English as a second language, the School of Professional Studies transforms knowledge and understanding in service of the greater good.
The School of Professional Studies seeks experienced industry professionals to serve as part-time Lecturer for a graduate-level course in Applied Deep Learning and AI. This advanced course delves into deep learning, blending key elements from Statistical Machine Learning. Students will gain a solid foundation in supervised learning and other related algorithms and methods. Topics covered include Support Vector Machines, Neural Networks, Convolutional Neural Networks (CNN), word embeddings, attention mechanisms, transformers, encoder-decoder architectures, Generative Adversial Networks (GAN), and Reinforcement Learning. Practical applications will demonstrate how to prepare, train, test, and validate models. Lecturers are the primary instructors for courses and an invaluable component of the faculty community.
Responsibilities
• Lead class lectures, instructional activities, and classroom discussions. Attend all class sessions.
• Monitor and address student concerns and inquiries.
• Evaluate and grade student work and assessments.
• Conduct office hours.
Columbia University SPS operates under a scholar-practitioner faculty model, which enables students to learn from faculty possessing outstanding academic training and a record of accomplishment as practitioners in an applied industry setting. 
Requirements
• Doctorate degree in Computer Science, Data Science, or a related field.
• Proficient in Python and familiar with deep learning frameworks (e.g., TensorFlow, PyTorch).
• Deep Learning Knowledge: Strong understanding of CNNs, RNNs, LLMs, Reinforcement Learning, and model evaluation techniques.
• Hands-on experience with deep learning projects and data manipulation.
Preferred Skills & Experience
• 10+ years of related professional experience. 
• 2+ years of University teaching experience, ideally at the graduate level.
• Strong verbal and written skills for explaining concepts clearly.
Please submit a resume inclusive of university teaching experience.
Columbia University is an Equal Opportunity/Affirmative Action employer.
All your information will be kept confidential according to EEO guidelines.
Salary range: $11,000 - $13,000 per semester long course