Japanese-Speaking Medical AI Reviewer

You will review medical materials and AI-generated outputs for clinical review projects. Your feedback will help assess quality and improve how AI systems handle healthcare information.

• Classify source materials and review them against project guidelines.
• Evaluate AI outputs involving clinical scenarios, patient cases, healthcare data, and medical documentation.
• Explain your quality judgments clearly in writing.
• Apply project rubrics consistently across repeated review tasks.
• Maintain careful, consistent work while completing tasks independently.

What It Pays And Takes

This is an entry-level, part-time contractor role. Previous AI training experience is not required, but you need relevant knowledge of medical AI clinical review and strong written communication.

• Pay: $125 per hour.
• Schedule: Part time, with less than 20 hours per week.
• Work arrangement: Remote contractor tasks.
• Location: Open to candidates in Algeria, Argentina, Austria, Bahrain, Bangladesh, Belgium, Brazil, Canada, Chile, Colombia, Denmark, Egypt, France, Germany, Ghana, Greece, India, Indonesia, Italy, Japan, Jordan, Kazakhstan, Kenya, Kuwait, Kyrgyzstan, Lebanon, Malaysia, Mexico, Morocco, the Netherlands, Nigeria, Oman, Pakistan, Palestinian Territories, Peru, the Philippines, Portugal, Qatar, Saudi Arabia, Singapore, South Africa, Spain, Sri Lanka, Switzerland, Taiwan, Thailand, Tunisia, the United Arab Emirates, the United Kingdom, the United States, Uzbekistan, and Vietnam.
• Language: Fluent in Japanese and English.
• Requirements: Relevant professional, academic, or language expertise in medical AI clinical review.
• Requirements: Strong attention to detail and the ability to follow detailed instructions and apply rubrics consistently.
• Estimated project earnings: $15,000 based on the listed schedule and duration.

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 reviewed examples, including medical documents, model responses, and structured feedback. People with medical knowledge and strong language skills help assess whether AI outputs are accurate, clear, and useful.