Part-Time Lecturer - Applied Machine Intelligence

About the Opportunity

The College of Professional Studies at Northeastern University invites applications for a non-tenure track part time faculty lecturer in Applied Machine Intelligence on the Miami campus.

Responsibilities

Qualified candidates must be prepared to work with global student populations. The Master of Professional Studies in Applied AI is looking for part-time faculty to teach courses across the program with special emphasis on applied artificial intelligence, machine learning, natural language processing, computer vision, and experiential learning addressing business and technical challenges in industry.

Instructional areas include, but are not limited to:

• Core AI courses: Applied Artificial Intelligence, Applied Machine Learning, Applied Natural Language Processing, Applied Computer Vision
• Specialized areas: Deep Learning, Generative AI, Prompt Engineering, Conversational AI and Chatbots, Reinforcement Learning
• Applied domains: Machine Learning for Cybersecurity, AI for 3D Imaging, Recommender Systems, Quantum AI, Blockchain AI, AI for Autonomous Systems
• Foundational courses (for Connect pathway students): Python programming, Mathematical Concepts, Research Methods and Scientific Writing

Qualifications

Minimum qualifications: Terminal degree (Ph.D.) in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or an aligned discipline. Demonstrated professional experience implementing AI/ML solutions in industry settings. Proficiency in Python and modern AI/ML frameworks (TensorFlow, PyTorch, scikit-learn, or similar).

Strongly preferred: 5+ years of professional experience in AI/ML development, implementation, or strategy roles. Demonstration of teaching, coaching, and/or training experience with a history of successful teaching (online/on-ground) at the graduate level strongly preferred. Experience teaching adult learners or working professionals. Expertise in one or more specialized areas. Understanding of AI ethics, governance, and responsible AI practices.