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Mercor in the United States is seeking native or near-native Hindi speakers to audit AI training data in Hindi. You will work across two audit collections, evaluating transcriptions and verifying word-level timing and accuracy.
You will complete a short calibration phase before production work begins, and all judgments require written justification using a fixed rulebook in English. Strong attention to detail is essential.
We are hiring native and near-native Hindi speakers to audit AI training data in their own language, not to produce it.
You will work across two Amazon Sonic audit collections:
Native or near-native command of Hindi as spoken in India. You were either born and raised in a region where this language and variety is dominant, or you are fully fluent with five or more years of residence in such a region. This is a hard requirement.
Strong English reading and writing. All conventions, error codes, and audit rationale are written in English, so you need to read a detailed rulebook and write clear feedback in it.
Disciplined rubric application. The work rewards people who apply a written standard consistently across hundreds of judgments rather than relying on instinct. Prior transcription, subtitling, localization, linguistic annotation, or QA experience is a strong signal.
Careful listening. You can distinguish similar sounds, identify where one word ends and the next begins in continuous speech, and tell genuine acoustic ambiguity apart from a clear annotator error.
Helpful but not required: prior AI data annotation or model evaluation work, a phonetics or linguistics background, and experience with audio editing or waveform tools.
You listen, judge, and submit. Where you find an error you tag every applicable error code, not just the first one, and you explain in plain language why the task passed or failed. Critical errors are called out first so the reason for a failure is obvious to the reader.
You will complete a short calibration phase before production work begins.