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Brandeis

Temporary Micro-Credential Grader – Industry-Focused Prompt Engineering for ROI-Driven Results

Salary$21k – $31k
LocationBrandeis - Waltham Campus
Work moderemote
Typepart-time
DepartmentRabb School of Continuing Studies
Company size10,000+ people
First seen1w ago
Last seen4d ago
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
About the role
Brandeis University’s Rabb School of Continuing Studies is seeking a detail-oriented professional with expertise in applied AI to serve as a Micro-Credential Grader for the online asynchronous credential, Industry-Focused Prompt Engineering for ROI-Driven Results.
In this fully remote, short-term hourly position, you’ll evaluate learner submissions that demonstrate practical mastery in designing, testing, and refining prompts for large language models (LLMs) to support measurable organizational objectives. This credential equips professionals with the ability to align prompt strategies with business goals, evaluate platform performance, and quantify the ROI impact of AI-driven solutions. As a grader, you’ll apply structured rubrics to assess strategic thinking, prompt engineering fluency, and outcome-based reasoning.
This role offers a unique opportunity to contribute to a cutting-edge, workforce-aligned credential that bridges AI innovation with business impact.
What You Will Do
•
Evaluate learner submissions that include prompt design portfolios, platform performance analyses, and ROI impact assessments tied to real-world business use cases.
•
Apply structured rubrics to assess mastery of skills such as LLM prompt iteration, use-case alignment, performance benchmarking, and ROI quantification.
•
Review learner reflections on prompt strategy effectiveness, ethical considerations, and organizational integration.
•
Participate in calibration exercises with fellow graders (if needed) to ensure consistency in evaluating strategic, technical, and business-oriented artifacts.
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Maintain confidentiality and objectivity throughout the grading process.
What You Bring
Nice to have
•
Bachelor’s degree required; Master’s degree preferred in Computer Science, Data Science, Business Analytics, or related disciplines.
•
Subject-matter expertise in prompt engineering, LLM capabilities, and AI deployment for business outcomes.
•
Experience in academic assessment, workforce development, or digital learning preferred.
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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.
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Proficient in rubric-based assessment and competency validation, especially for strategic and project-based submissions.
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Strong attention to detail and ability to maintain consistency across diverse learner artifacts.
•
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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