The work
You will transcribe German audio while preserving the original meaning, tone, and context. You will also review transcripts, add useful details, and help keep quality consistent across the project.
- Listen to German audio and create accurate transcripts.
- Review and edit transcripts using linguistic and style guidelines.
- Add speaker names or labels, timestamps, and context notes.
- Run quality checks for accuracy and consistency.
- Work with project stakeholders to resolve unclear language.
- Suggest improvements to transcription processes and guidelines.
What it pays and takes
This is an entry-level project, and prior AI experience is not required. The work does require language-focused experience, careful attention to detail, and the ability to follow written guidelines precisely.
- Pay: $20 to $30 per hour equivalent, paid per task.
- Schedule: 20+ hours per week.
- Language: Professional fluency in German, including regional dialects and variations.
- Location: Open to candidates in the United Arab Emirates, Argentina, Austria, Bangladesh, Belgium, Bahrain, Brazil, Canada, Switzerland, Chile, Colombia, Germany, Denmark, Algeria, Egypt, Spain, France, and the United Kingdom.
- Location: Also open to candidates in Ghana, Greece, Indonesia, India, Italy, Jordan, Japan, Kenya, Kyrgyzstan, Kuwait, Kazakhstan, Lebanon, Sri Lanka, Morocco, Mexico, Malaysia, Nigeria, the Netherlands, Oman, Peru, the Philippines, Pakistan, Palestine, Portugal, Qatar, Saudi Arabia, Singapore, Thailand, Tunisia, Taiwan, the United States, Uzbekistan, Vietnam, and South Africa.
- Experience: Demonstrable experience in transcription, linguistics, or related language-focused work.
- Tools: Comfortable using audio playback and transcription tools, plus standard office software.
- Helpful background: Familiarity with linguistic annotation and metadata practices.
How it works
Apply on OpenTrain with your resume and then complete the application on the hiring site.
About AI training work
AI training work uses examples prepared and reviewed by people to help artificial intelligence systems understand language and produce better results. Experienced language contributors are valuable because their careful judgments help make training data accurate, consistent, and useful.