Financial Aid Advisor for Federal Student Loans

The Work

You will help improve AI systems that answer federal student loan questions. You will review sample borrower questions and assess whether generated answers are accurate, complete, useful, and aligned with current guidance.

You will create rubrics, which are clear standards for judging answers, and use them to identify errors, missing information, and outdated guidance. You will also provide written feedback to improve answer quality.

• Review questions to identify concerns that do not sound like authentic borrower situations.
• Create rubrics for repayment, income-driven repayment plans, and forgiveness programs such as Public Service Loan Forgiveness.
• Assess answers about loan servicer issues, deferment, forbearance, and default management.
• Flag inaccurate, incomplete, or outdated information.
• Write clear feedback that explains how answers should improve.

What It Pays and Takes

This is a remote, short-term contractor project with asynchronous collaboration. The work is marked as entry level, while the role requires direct experience supporting federal student loan borrowers.

• $30-$50 per hour.
• 15-25 hours per week.
• Remote project open to candidates in the United States.
• Professional fluency in English.
• At least one year advising borrowers on income-driven repayment plans, Public Service Loan Forgiveness, or other repayment and relief options.
• Experience must include repayment, forgiveness, servicer issues, or default resolution, not only financial aid award packaging.
• Working knowledge of loan servicing, deferment, forbearance, and default.
• Clear and precise written communication.
• Experience in a nonprofit counseling organization, university financial aid office, loan servicing environment, or similar borrower-support setting is helpful.
• No prior AI experience is required.

How It Works

Apply on OpenTrain with your resume, then complete the application on the hiring site.

About AI Training Work

AI training is the human work behind systems that answer questions and generate content. People with subject-matter expertise review examples, define what good answers look like, and give feedback so AI systems become more accurate and useful.