About the Role
We're looking for experienced accounting instructors to help shape how AI understands financial concepts. Your expertise will directly influence the accuracy and quality of AI systems trained on accounting principles — from financial statements to internal controls. This is a rare opportunity to make a meaningful impact on cutting-edge technology from the comfort of your own home.
- Organization: Alignerr (Powered by Labelbox)
- Type: Hourly Contract
- Location: Remote
- Commitment: 10–40 hours/week
What You'll Do
- Review and evaluate accounting questions, explanations, and worked examples used in AI training datasets
- Assess the accuracy of key concepts including financial statements, bookkeeping, managerial accounting, and internal controls
- Identify errors, edge cases, logical gaps, and misleading explanations
- Provide clear, structured feedback to improve the precision and pedagogical quality of AI-generated content
- Work independently and asynchronously on a schedule that fits your life
Who You Are
- 3+ years of experience teaching accounting at the postsecondary level
- Deep understanding of both financial and managerial accounting principles
- Skilled at evaluating written quantitative and rule-based reasoning for accuracy and clarity
- Comfortable reviewing structured content at scale with consistent attention to detail
- Self-motivated and reliable in an asynchronous, remote work environment
Nice to Have
- CPA designation or Master's degree in Accounting
- Hands-on experience with audits, financial analysis, or applied accounting practice
- Familiarity with AI systems, model evaluation, or data annotation workflows
Why Join Us
- Work on cutting-edge AI projects with top research labs and technology teams
- Fully remote and flexible — set your own hours and work at your own pace
- Freelance perks: autonomy, variety, and collaboration with a global expert community
- Directly contribute to improving how AI handles complex financial reasoning
- Potential for ongoing work and contract extension based on performance