The work
You will help improve language models by reviewing written content and model answers. The work includes research, analytical question answering, scenario creation, and detailed feedback in English and Spanish.
The title identifies this as a Business Analyst role, while the described work focuses on multilingual AI content evaluation. Review the duties and qualifications before applying.
- Read, summarize, and organize written content into clear logical sections.
- Research information online and validate claims.
- Answer analytical and reasoning questions with accurate explanations.
- Create scenarios and examples for language model training.
- Review model answers for errors and provide constructive feedback.
- Produce detailed annotations and communicate decisions clearly.
- Work with examples involving sales trends, logical constraints, data interpretation, and basic arithmetic.
What it pays and takes
This is a remote, part-time contractor assignment with a short-term contract. Pay details are not provided in the listing.
- Time commitment: At least 20 hours per week.
- Schedule: Some overlap with US hours is required.
- Location: Open worldwide.
- Languages: Professional capability in Spanish and English.
- Experience level: Entry level.
- Skills: Strong research, analysis, data interpretation, logical reasoning, basic arithmetic, and clear written communication.
- Work style: Creative and lateral thinking, collaboration, self-motivation, and the ability to work independently in a remote setting.
- Equipment: A reliable desktop or laptop and an internet connection.
- Helpful background: Business analysis, research, journalism, copywriting, technical writing, editing, or translation.
- Helpful education: Study or a degree in engineering, literature, journalism, communications, arts, statistics, or a related field.
- Useful tools: Excel and Google Suite.
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. People prepare examples, review model answers, and explain what is accurate or useful so these systems improve.