The work
You will review Spanish audio clips and model outputs to judge whether speech sounds fluent, natural, and linguistically authentic. Your work will support improvements in how language systems understand and generate Spanish.
You will explain your decisions in clear English, describe the linguistic factors behind each rating, and flag unclear language content when project requirements need interpretation. Accuracy, consistent judgment, and on-time completion are important.
- Listen to Spanish audio clips and evaluate fluency, naturalness, pronunciation, and intonation.
- Rate speech and model outputs using established project guidelines.
- Write clear English feedback and detailed reasons for each decision.
- Clarify ambiguous language content with project coordinators when needed.
What it pays and takes
This is a remote, part-time contract opportunity for candidates in Mexico.
- Pay: $30 to $65 per hour.
- Location: Open to candidates in Mexico.
- Work arrangement: Remote, part-time contract.
- Language: Professional fluency in spoken and written Spanish.
- English: At least B2 proficiency for written and verbal communication.
- Bring strong attention to detail and the ability to explain nuanced language differences.
- Relevant backgrounds include linguistics, phonetics, voice work, audio engineering, audio analysis, transcription, or linguistic assessment.
- Helpful experience includes media, communications, teaching, music, or other audio-focused work.
How it works
You apply here in about two minutes and finish on the hiring platform. The next step is a short AI interview (technical roles include a coding challenge). Passing it is how you qualify for paid tasks, so take it soon after you are invited.
The hiring platform's application for this job asks for your resume, your LinkedIn profile, your phone number, your location and answers to 3 screening questions.
About AI training work
AI training is the human work behind artificial intelligence, including reviewing audio, text, images, and model responses. People use language and subject knowledge to prepare examples and judge results so AI systems can improve. Experienced contributors are often needed when evaluations depend on careful listening and nuanced judgment.