Vietnamese-Speaking AI Translation and Evaluation Expert

You will review AI-generated Vietnamese content and judge whether responses are correct, clear, well reasoned, and suitable for Vietnam. You will compare multiple answers, identify errors, and explain what a better response should do.

The role combines translation, localization, linguistic quality review, fact-checking, and AI response evaluation. You will use MQM or LQA-style standards to keep your reviews consistent.

• Review and improve AI-generated Vietnamese language content.
• Evaluate reasoning quality, logical flow, and step-by-step problem solving.
• Check accuracy, terminology, clarity, tone, inclusivity, and cultural nuance.
• Fact-check names, dates, references, and other localized details.
• Rate and compare multiple AI responses using clear quality judgments.
• Write explanations that show correct methods and identify conceptual or methodological errors.

What it pays and takes

This is remote, part-time contractor work for candidates based in Vietnam. The role is listed as entry level, but it requires strong Vietnamese language and translation quality skills.

• Pay: Up to $20 per hour.
• Schedule: About 20 hours per week for about 8 weeks.
• Location: Vietnam hiring location; remote work.
• Language: Fluent Vietnamese with strong awareness of the Vietnam locale.
• English: Minimum C1 reading and writing ability.
• Experience: Vietnamese translation, localization, or linguistic QA experience.
• Tools and methods: CAT tools, QA workflows, terminology management, style guides, and MQM or LQA concepts.
• Skills: Strong editorial judgment for register, tone, inclusivity, and cultural nuance.
• Preferred: Previous AI data training, annotation, or evaluation experience.
• Work style: Dependable throughput and clear documentation as an hourly contractor.

How it works

Apply on OpenTrain with your resume, then complete the application on the hiring site.

About AI training work

AI training work is the human side of building artificial intelligence. People review examples and model responses so AI systems can become more accurate, useful, and appropriate across languages and cultures.