The work
You will review AI-generated answers to business decisions, strategic questions, and operational scenarios. Your feedback will help improve how AI models reason about practical business problems.
You will use evaluation rubrics to judge the quality of reasoning, assumptions, analysis, and recommendations. You will also identify gaps, edge cases, and blind spots, then write detailed annotations and feedback.
You will work with researchers and project managers to keep evaluation standards aligned with project goals.
- Review AI-generated responses about business strategy and operations.
- Rate reasoning quality, assumptions, analysis, and practical recommendations.
- Find gaps, edge cases, and weaknesses in strategic thinking.
- Write clear, detailed annotations and feedback.
- Collaborate with researchers and project managers on evaluation standards.
What it pays and takes
This is a remote, short-term contractor engagement. The work involves evaluating written AI responses, so strong written communication and careful judgment are important.
- Pay: $150 per hour.
- Schedule: 20+ hours per week.
- Engagement: 40 total hours over 4 weeks.
- Location: Remote and open worldwide.
- Language: Fluency in English.
- Experience level: Entry level.
- Experience: At least 3 years in management consulting, business strategy, operations, or a related generalist role.
- Skills: Analyze unclear business problems and develop practical recommendations.
- Knowledge: Business models, market analysis, process improvement, and organizational decision-making.
- Required: Excellent written communication and close attention to detail.
- Helpful: Previous AI annotation experience.
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 better AI systems. People review and rate model responses, explain what works or fails, and provide examples that help models improve.
Business strategy evaluators bring practical judgment to this process by checking whether AI recommendations are sound, realistic, and useful.