Review and correct machine-generated Spanish (Spain) audio transcriptions, add metadata, and annotate language data remotely. Earn $10 to $20 per hour with a flexible schedule of 20 or more hours weekly.
The Work
You will review machine-generated Spanish (Spain) transcriptions and improve their accuracy, fluency, and quality. You will also add metadata and linguistic notes to audio and text datasets used to train AI systems.
The role involves working independently, resolving unclear language with the project team, and finding inconsistencies that could improve the data or the review process.
- Correct Spanish (Spain) audio transcriptions for accuracy and natural language.
- Add metadata tags and detailed linguistic annotations to audio and text data.
- Check content against language guidelines and project requirements.
- Help resolve language ambiguities and improve dataset quality.
- Identify inconsistencies and suggest process improvements.
What It Pays and Takes
This is a remote, part-time contractor role for people with strong Spanish (Spain) language skills. No prior AI experience is required, but you should be comfortable reviewing machine-generated language output and managing language data with digital tools.
- Pay: $10 to $20 per hour.
- Schedule: Flexible, with a requirement of 20 or more hours per week.
- Location: Remote and available in the listed countries.
- Language: Fluent Spanish from Spain with strong linguistic judgment.
- Experience: Transcription, annotation, and metadata tagging experience required.
- Requirements: Excellent attention to detail, strong written and spoken communication, and the ability to meet strict deadlines with little supervision.
- Helpful background: Audio engineering, professional transcription, data annotation, linguistics, or experience with AI transcription tools.
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 work behind systems that understand audio and language. People review, correct, and label examples so AI models can learn from accurate data, and language specialists are paid for the judgment and attention this requires.