The work
You will help improve AI systems that answer federal student loan questions. You will use your borrower counseling experience to judge whether questions sound realistic and whether generated answers are accurate, useful, complete, and based on current guidance.
- Review sample federal student loan questions and identify concerns that do not sound like real borrower needs.
- Create rubrics for correct and complete answers about repayment, income-driven repayment plans, and forgiveness programs such as Public Service Loan Forgiveness.
- Cover loan servicing issues, deferment, forbearance, and default management in your rubrics.
- Evaluate AI-generated answers against the rubrics and flag errors, missing information, and outdated guidance.
- Write clear feedback that helps improve answer quality.
What it pays and takes
This is a remote, short-term contractor project with asynchronous collaboration. No prior AI experience is required, but strong federal student loan knowledge and direct borrower counseling experience are required.
- Pay: $30 to $50 per hour.
- Time: 15 to 25 hours per week.
- Location: Open to candidates in the United States.
- Work arrangement: Remote, part-time contract project.
- Experience: At least one year advising on income-driven repayment plans, Public Service Loan Forgiveness, or other repayment and relief options.
- Required knowledge: Federal student loan repayment and forgiveness, loan servicing, deferment, forbearance, and default resolution.
- Your experience must extend beyond award packaging into repayment, forgiveness, servicer issues, or default resolution.
- Required skill: Clear and precise written communication.
- Relevant backgrounds include nonprofit borrower counseling, university financial aid, loan servicing, or similar borrower-support work.
How it works
Apply on OpenTrain with your resume, then complete the application on the hiring site. OpenTrain is free to join and helps people find AI training and data-labeling projects in one place.
About AI training work
AI training is the human work behind systems that learn from examples, including reviewing and rating generated answers. People with subject-matter experience help make those answers more accurate, useful, and aligned with real-world needs.