Temporary Micro-Credential Grader - JSD- Microcredential Grader-AI Fundamentals for STEM Professionals job opportunity at Brandeis University.

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Brandeis University Temporary Micro-Credential Grader - JSD- Microcredential Grader-AI Fundamentals for STEM Professionals
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loacation Brandeis - Waltham Campus, United States Of America
loacation Brandeis - Wal..........United States Of America

Position: Micro-Credential Grader-AI Fundamentals for STEM Professionals Location: Fully remote (U.S.-based applicants only, no visa sponsorships) Division: Rabb School of Continuing Studies, Brandeis University Type: Part-Time, 4 months, varying hours, no more than 25 hours per week Compensation: Hourly $25-$30 Reports to: Assistant Dean of Education and Learning Innovation Brandeis University’s Rabb School of Continuing Studies is seeking a detail-oriented STEM professional to serve as a Micro-Credential Grader for the online asynchronous credential, AI Fundamentals for STEM Professionals. In this fully remote, short-term hourly position, you’ll evaluate learner submissions that demonstrate mastery of AI concepts through a real-world STEM challenge and a complete 5-step workflow design. This project-based credential equips professionals with foundational skills in supervised learning, data preprocessing, model selection, and ethical AI deployment. As a grader, you’ll apply structured rubrics to assess technical accuracy, conceptual depth, and responsible innovation. This role offers a unique opportunity to contribute to a high-impact, workforce-aligned credential that bridges STEM expertise with emerging AI capabilities. What You Will Do: Evaluate learner submissions of the AI Workflow Project, which include a real-world STEM challenge, an AI-powered solution, and a complete 5-step workflow design. Apply structured rubrics to assess mastery of skills such as supervised learning, data preprocessing, model selection, and interpretability. Participate in calibration exercises with fellow graders (if needed) to ensure consistency in evaluating technical artifacts and conceptual reasoning. Maintain confidentiality and objectivity throughout the grading process What You Bring: Bachelor’s degree required; Master’s degree preferred in Computer Science, Data Science, Engineering, or related STEM disciplines. Subject-matter expertise in foundational AI concepts, including machine learning, data analysis, and ethical considerations in AI deployment. Experience in academic assessment, workforce development, or digital learning preferred. Familiarity with learning management systems (Moodle preferred), online credentialing platforms, and collaborative grading workflows. Professional, learner-centered approach with a commitment to academic integrity and continuous improvement. Proficient in rubric-based assessment and competency validation, especially for technical and project-based submissions. Strong attention to detail and ability to maintain consistency across diverse submissions. Excellent written communication skills for delivering constructive, learner-focused feedback. Comfortable working in asynchronous learning environments and using digital platforms. Adaptability in managing multiple grading tasks within deadlines. 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").

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