Mach aus dieser Rolle ein Bewerbungsgespräch — ein Lebenslauf und ein Anschreiben, die darauf ausgerichtet sind, was dieser Arbeitgeber sucht.
Mercor is hiring native or near-native German speakers to audit AI training data in German. You will evaluate transcripts for accuracy, apply error codes, and write concise rationales.
The role covers transcription audits and word-by-word alignment checks, with a calibration phase before production work. Requirements include native or near-native German, fluent English reading/writing, and disciplined rubric application.
We are hiring native and near-native German speakers to audit AI training data in their own language, not to produce it.
You will work across two Amazon Sonic audit collections:
Both collections run at 5.00 audit hours per task. Every judgment carries a written reason, so this is careful listening work rather than volume work.
Native or near-native command of German as spoken in Germany. 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 starts.