Adjunct Instructor in Machine Learning Engineering and MLOps

Brandeis University•Waltham, MA

2d•$6,573•Remote

About The Position

Brandeis University’s Online Applied Data Science and Decision Analytics Program is seeking an Adjunct Faculty member for RADS 110 Machine Learning Engineering and MLOps for the Fall 1 2026 session. This 3-credit asynchronous online course is an 8-week requirement for the Master of Science in Applied Data Science and Decision Analytics. Course Description This course focuses on the end-to-end ML lifecycle—feature stores, CI/CD pipelines, containerization, and model monitoring emphasizing responsible, scalable deployment.

Requirements

Advanced degree (Masters or Ph.D) in Computer Science, Data Science, or Software Engineering)
Industry experience in MLOPS, ML Platform development, production of ML systems or related fields.
Experience in end-to-end machine learning engineering, including feature engineering model training, containerization, model monitoring, and responsible deployment practices.
At least 1 year of teaching or training experience (preferably online/asynchronous)
Experience with online instruction
Excellent communication and teaching skills in an online learning environment.

Nice To Haves

Prior online teaching experience at the graduate level
Knowledge of global learner personas and culturally responsive pedagogy
Familiarity with Moodle LMS and digital authoring tools (e.g., H5P)

Responsibilities

Focuses on the organized and timely rollout of course content, maintaining consistent communication through weekly announcements, and ensuring all instructional activities occur within university-approved digital platforms.
Centers on building an active teaching persona by hosting live introductory sessions, facilitating weekly academic discourse in forums, and maintaining regular availability for student consultation.
Emphasizes the professional obligation to provide transparent, rubric-based evaluations and supportive commentary on student work within a standardized weekly timeframe.
Requires adherence to university communication protocols, the promotion of respectful online "netiquette," and ensuring the course meets accessibility and technical visibility standards before and during the term.