Adjunct Associate Faculty, Applied Generative AI (On-Campus, Fall '26)
Seeking analytics professionals to serve as a part-time Associate for a graduate-level course on Applied Generative AI. An Associate is a faculty line junior to a Lecturer, that provides subject matter expertise and supports the instructional process for a course section. Serving as an Associate is an outstanding way to gain exposure to graduate-level teaching at Columbia University.
The Applied Generative AI course provides students with a comprehensive introduction to a branch of machine learning called generative modeling, focusing on the underlying concepts, theoretical techniques, and practical applications. Students will learn to use, fine-tune, and programmatically interface with high-level APIs and open-source foundational models, allowing them to leverage state-of-the-art tools in Generative AI. Additionally, the course delves into the theory and practice of low-level implementations, empowering students to train their own models on their own data and understand these models from first principles. The course covers various types of generative models, including Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Transformers with their applications to text, image, audio, and video generation.
Responsibilities
Attend all class sessions, assist with instruction, lead breakout sessions, facilitate discussions.
Evaluate, grade student work and assessments as requested by the course Lecturer.
Monitor and address student concerns and inquiries.
Qualifications
Columbia University SPS operates under a scholar-practitioner faculty model, which enables students to learn from faculty possessing outstanding academic training as well as a record of accomplishment as practitioners in an applied industry setting.
Requirements
Graduate degree in an area related to Machine Learning, Computer Science, Applied Mathematics, or related field.
3+ years of related applied professional experience.
Preferred Skills & Experience
Programming experience in Python and experience with major deep learning frameworks such as PyTorch or TensorFlow.
Knowledge of deep learning architectures, such as CNNs, VAEs, GANs, and RNNs.
Experience with deploying code on cloud platforms such as AWS, GCP, or Azure.
Knowledge of Mathematics and Probability concepts used in machine learning, including
Optimization, Gradient Descent, Conditional Probability, Bayes Theorem, and Normal Distribution.
Additional Information
Please submit a resume inclusive of university teaching experience.
All your information will be kept confidential according to EEO guidelines.
Columbia University is an Equal Opportunity/Affirmative Action employer.