Quantitative Finance AI Research Evaluator

You will help improve AI systems by creating expert-level quantitative finance training data and reviewing AI-generated answers. Your feedback will focus on whether responses are accurate, relevant, and consistent with accepted quantitative finance methods.

The work is remote, flexible, and asynchronous. You will work independently on an ongoing, project-based contract.

• Create training data based on quantitative finance research and academic knowledge.
• Check AI-generated responses for technical accuracy, relevance, and methodological fit.
• Review topics such as computational finance, portfolio management, risk management, statistical finance, trading, and market microstructure.
• Write clear feedback that helps improve AI understanding of quantitative finance research.
• Use arXiv, including its quantitative finance repository, q-fin, as part of your research work.

What It Pays and Takes

This opportunity is for people with strong quantitative finance knowledge and research skills. No prior AI experience is required, but you must be able to assess technical financial content carefully.

• Pay: $150 per hour.
• Location: United States.
• Language: English.
• Work type: Remote, part-time contract work.
• Schedule: Flexible and asynchronous; the role is listed as 20 or more hours per week, while contributors commonly work about 5 to 20 hours during active projects.
• Education: Master's or PhD in quantitative finance, mathematical finance, or financial engineering.
• Required skills: Strong written communication, research ability, technical accuracy checks, independent work, and careful attention to detail.
• Helpful background: Computational or mathematical finance, portfolio management, risk management, statistical finance, trading, or market microstructure.

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 generate and understand text. Contributors create examples, review model answers, and explain what is accurate or useful so AI systems can improve; people with specialized knowledge are paid for applying that expertise.