You will create training and evaluation data for large language models. This means writing realistic conversations between users and AI assistants, modeling both sides of each exchange, and showing when an assistant should use a tool, complete a task, explain a limitation, or continue without one.
You will work across everyday scenarios involving calendars, email, maps, and cloud drives. Your examples should reflect changing user goals, natural dialogue, feasible and infeasible requests, and logical tool-based workflows.
- Write multi-turn user and assistant conversations.
- Create corrective or simulated function calls when they fit the situation.
- Choose appropriate function calls and maintain logical flows across tools.
- Preserve natural general conversation alongside structured tool use.
- Follow detailed formatting and quality guidance.
- Revise examples using feedback and collaborate with peers and reviewers.
What It Pays and Takes
This is a part-time remote contractor assignment planned for 12 weeks. The listing does not provide a pay rate.
- Time commitment: 20+ hours per week.
- Work arrangement: Remote contractor assignment.
- Available locations: India, Pakistan, Nigeria, Egypt, Ghana, Bangladesh, Turkey, Brazil, and Mexico.
- Language: Excellent written English.
- Experience: At least three years of professional experience in a technical or analytical field.
- Required skills: Technical reasoning, API and JSON knowledge, familiarity with data formats, and understanding of logical tool-based workflows.
- Communication: Strong control of clarity, tone, and instructional coherence.
- Helpful experience: Python, Java, JavaScript, LLMs, virtual assistants, function-calling frameworks, conversational datasets, model evaluation, or structured example writing.
- Helpful familiarity: Assistant workflows involving calendars, email, maps, or cloud storage.
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 learn from examples, including writing conversations and reviewing how models respond. People with strong technical reasoning and careful judgment help make those examples realistic, consistent, and useful.