Adjunct Instructor in Machine Learning Engineering and MLOps

Adjunct Instructor in Machine Learning Engineering and MLOps Brandeis University - 4.4 Waltham, MA Job Details Part-time $6,573.15 a month 18 hours ago Qualifications AI models Containerization systems Software engineering Online learning environment experience Production systems Model deployment Instructor-led training (training delivery method) Online training platform teaching experience Model training Online instruction Machine learning (ML) fundamentals Teaching engineering Remote training MLOps Full Job Description 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.

Core Course Responsibilities Summary Course Logistics and Facilitation:

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.

Instructor Presence and Engagement:

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.

Individual Feedback and Grading:

Emphasizes the professional obligation to provide transparent, rubric-based evaluations and supportive commentary on student work within a standardized weekly timeframe.

Professional Conduct and Standards:

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.

Qualifications:

Required:

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.

Preferred:

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) Interested candidates should submit: A cover letter highlighting relevant qualifications and teaching experience. A current CV or resume. Contact information for three professional references. Application review begins 5/27/2026 though we will continue to accept submissions on an ongoing basis. This appointment is to a position that is in a collective bargaining unit represented by SEIU Local 509. Compensation for this position is $6573.15 Pay Range Disclosure The University's pay ranges represent a good faith estimate of what Brandeis reasonably expects to pay for a position at the time of posting. The pay offered to a selected candidate during hiring will be based on factors such as (but not limited to) the scope and responsibilities of the position, the candidate's work experience and education/training, internal peer equity, and applicable legal requirements. Equal Opportunity Statement Brandeis University is an equal opportunity employer which does not discriminate against any applicant or employee on the basis of race, color, ancestry, religious creed, gender identity and expression, national or ethnic origin, sex, sexual orientation, pregnancy, age, genetic information, disability, caste, military or veteran status or any other category protected by law (also known as membership in a "protected class").