US Personal Finance Task Contributor

You will complete structured assignments that help AI systems understand US household budgeting, tax filing, and digital personal finance workflows. The work involves entering and checking financial information, following detailed instructions, and documenting each process clearly.

• Complete at least four assignments based on US household budgeting and tax filing workflows.
• Connect a US-based financial account through Plaid, with personally identifiable information redacted before downstream use.
• Enter and verify financial data using online banking platforms, finance applications, and basic spreadsheets.
• Document step-by-step processes with clear written explanations.
• Follow project instructions and quality standards while working independently.

What It Pays and Takes

This is a short-term, remote contractor project. Prior AI training experience is not required, but you should be comfortable handling personal finance workflows carefully and following detailed written instructions.

• Pay: One-time payment of $2,535 upon meeting the project requirements.
• Project deadline: Complete all tasks by October 21, 2026.
• Location: Open to candidates currently residing in the United States.
• Account requirement: Hold a US-based financial account.
• Experience requirement: Have personally filed a 2025 US federal income tax return.
• Language: Fluent in English.
• Skills: Practical knowledge of household budgeting and digital personal finance tools, accurate data entry, careful attention to detail, and methodical documentation.
• Helpful tools experience: Online banking, financial applications, Google Sheets, or Excel.
• Communication: Clear written and verbal communication, with the ability to follow detailed instructions independently.

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 learn from examples, including financial workflows and written explanations. People with practical subject knowledge help make those examples accurate, consistent, and useful for improving AI systems.