AI Response Evaluation Specialist

You will review AI-generated responses and judge their accuracy, clarity, reasoning, and fit with the prompt. You will also help create training content and compare model outputs so AI systems produce more reliable answers.

The role uses Japanese translation and localization judgment to keep terminology, tone, and cultural nuance correct. You will work with text and support quality checks across different use cases.

• Review AI-generated answers for correctness, clarity, and logical reasoning.
• Compare multiple model responses and rate their quality and adherence to the prompt.
• Fact-check information and identify errors in methods or conceptual understanding.
• Write clear explanations and model solutions that show the correct approach.
• Create prompts and responses for AI training, model improvement, and integrity checks.
• Check responses for inaccuracies or bias.

What it pays and takes

This is a part-time contractor role for an intermediate-level contributor. The work requires dependable throughput and responsiveness across time zones.

• $40 per hour.
• At least 20 hours per week.
• Open to candidates in Japan.
• Fluent in Japanese.
• At least C1 English proficiency for reading and writing.
• Experience in Japanese translation or localization.
• Familiarity with MQM or LQA, including severity and root-cause thinking.
• Experience with CAT tools and quality assurance workflows.
• Strong editorial judgment, attention to detail, and fact-checking ability.
• A bachelor's degree or higher in Japanese translation, linguistics, localization, communications, or a related field is helpful.
• Experience with translation memory, termbases, and automated checks is helpful.
• Prior AI data training, annotation, or evaluation experience is helpful.

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. Contributors review examples, rank responses, write better answers, and check facts so models learn to be more accurate and useful; people with language and evaluation experience are paid for that specialized judgment.