品質保証(QA)スペシャリスト – LLMコンテンツ評価
2週間前
Tokyo Tokyo, 13, 日本
Wiz-AI
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Quality Assurance (QA) Specialist – LLM Content Evaluation
Company hiring:
Appen Japan
Location:
Tokyo, Japan Work Arrangement: On-site (東京都文京区小石川五丁目40-6エンリッチビル2階)
About The Role
Appen is looking for 6 detail-oriented Quality Assurance (QA) Specialists to join our Large Language Model (LLM) project team in Japan. In this role, you will review and evaluate AI-generated content to ensure accuracy, quality, and consistency with established project guidelines. You will also check the quality of work completed by data annotators and provide feedback to help improve overall project quality. This position is ideal for someone who enjoys working with AI technologies, has a good eye for detail, and is interested in contributing to the development of Large Language Models (LLMs).
Key Responsibilities
- Review and evaluate AI-generated responses to ensure accuracy, relevance, and consistency with project guidelines.
- Conduct regular quality checks on data annotation and evaluation tasks completed by annotators.
- Identify errors, inconsistencies, and areas for improvement in AI-generated content and annotation results.
- Provide clear and constructive feedback to annotators to help improve their work quality.
- Check whether AI-generated responses are appropriate, helpful, and aligned with established evaluation standards.
- Track quality results and prepare simple quality reports.
- Work closely with team leaders and project managers to resolve quality-related issues.
- Participate in team discussions and training sessions to maintain consistent quality standards. Qualifications Required Qualifications
- Native or near-native proficiency in Japanese, with strong reading comprehension and written communication skills.
- Good English communication skills, with the ability to communicate effectively with international teams.
- Strong attention to detail and the ability to identify errors and inconsistencies.
- Good reading comprehension and analytical skills, with the ability to evaluate written content accurately.
- Basic computer skills, including Microsoft Excel, Google Sheets, or similar tools.
- Good communication skills and the ability to provide clear and helpful feedback.
- Ability to understand and follow project guidelines and quality standards.
- Ability to work independently and collaborate effectively with team members.
- Interest in AI technologies and Large Language Models (LLMs). Preferred Qualifications
- Previous experience in Quality Assurance (QA), data annotation, content evaluation, or a related field.
- Familiarity with AI tools and Large Language Models, such as ChatGPT.
- Experience reviewing written content or checking the quality of others' work.
- Experience working in a multicultural or international team. About Appen Appen is a global provider of high-quality data and AI-powered solutions, supporting leading technology companies in developing and improving artificial intelligence technologies. Our AI Data Quality teams help improve the performance and reliability of AI systems through data annotation, content evaluation, and quality assurance.
Location:
Tokyo, Japan Work Arrangement: On-site (東京都文京区小石川五丁目40-6エンリッチビル2階)
About The Role
Appen is looking for 6 detail-oriented Quality Assurance (QA) Specialists to join our Large Language Model (LLM) project team in Japan. In this role, you will review and evaluate AI-generated content to ensure accuracy, quality, and consistency with established project guidelines. You will also check the quality of work completed by data annotators and provide feedback to help improve overall project quality. This position is ideal for someone who enjoys working with AI technologies, has a good eye for detail, and is interested in contributing to the development of Large Language Models (LLMs).
Key Responsibilities
- Review and evaluate AI-generated responses to ensure accuracy, relevance, and consistency with project guidelines.
- Conduct regular quality checks on data annotation and evaluation tasks completed by annotators.
- Identify errors, inconsistencies, and areas for improvement in AI-generated content and annotation results.
- Provide clear and constructive feedback to annotators to help improve their work quality.
- Check whether AI-generated responses are appropriate, helpful, and aligned with established evaluation standards.
- Track quality results and prepare simple quality reports.
- Work closely with team leaders and project managers to resolve quality-related issues.
- Participate in team discussions and training sessions to maintain consistent quality standards. Qualifications Required Qualifications
- Native or near-native proficiency in Japanese, with strong reading comprehension and written communication skills.
- Good English communication skills, with the ability to communicate effectively with international teams.
- Strong attention to detail and the ability to identify errors and inconsistencies.
- Good reading comprehension and analytical skills, with the ability to evaluate written content accurately.
- Basic computer skills, including Microsoft Excel, Google Sheets, or similar tools.
- Good communication skills and the ability to provide clear and helpful feedback.
- Ability to understand and follow project guidelines and quality standards.
- Ability to work independently and collaborate effectively with team members.
- Interest in AI technologies and Large Language Models (LLMs). Preferred Qualifications
- Previous experience in Quality Assurance (QA), data annotation, content evaluation, or a related field.
- Familiarity with AI tools and Large Language Models, such as ChatGPT.
- Experience reviewing written content or checking the quality of others' work.
- Experience working in a multicultural or international team. About Appen Appen is a global provider of high-quality data and AI-powered solutions, supporting leading technology companies in developing and improving artificial intelligence technologies. Our AI Data Quality teams help improve the performance and reliability of AI systems through data annotation, content evaluation, and quality assurance.