English-Speaking Creative Data Annotation Generalist

You will help evaluate how a language model responds to personal, professional, and interpersonal situations. You will create realistic ongoing scenarios, interact with the model under assigned conditions, and review its advice.

Your written explanations will show whether each response is relevant, useful, personalized, clear, and suited to the context. You will follow project guidelines and complete assigned tasks within the required timeline.

• Create realistic social scenarios involving personal, professional, or interpersonal situations.
• Interact with a language model under assigned evaluation conditions.
• Rate responses for relevance, usefulness, personalization, clarity, and contextual alignment.
• Write clear, detailed reasons for your ratings.
• Complete assigned tasks within the project timeline.

What It Pays And Takes

This is a remote, short-term engagement expected to run for about 2 to 3 weeks. The project is expected to begin around October 1 or a nearby date, and each task takes about 60 minutes on average.

• Pay: Per completed label.
• Time: 20+ hours per week; the project description describes the engagement as full-time.
• Location: Remote and open worldwide.
• Language: Strong written English and clear communication skills.
• Thinking skills: Creative and analytical thinking, plus the ability to understand nuanced interpersonal situations.
• Relevant backgrounds: Business analysis, English, literature, journalism, creative writing, scriptwriting, content writing, film and video captioning, human-AI interaction, or a related field.
• Helpful experience: Data annotation, human-AI interaction, content evaluation, copywriting, data analysis, critical thinking, or similar analytical work.
• Experience level: Three or more years in a related area is helpful, but the role is listed as entry level.

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 language. People create examples, review model responses, and explain what makes an answer useful so AI systems can improve.