Remote German Language & Literature Specialist for AI

SaidGig

New York (NY)

Remote

USD 62,000 - 83,000

Part time

10 days ago

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Benefits offered by this job

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Job summary

SaidGig is seeking a native or near-native German linguist to evaluate and improve language models. You will review model-generated German content, edit for accuracy, and annotate texts to support research.

This remote, hourly engagement pays $50 per hour and offers hands-on experience with state-of-the-art AI development, contributing to translations, grammar, and stylistic improvements across diverse use cases.

Qualifications

  • Native or near-native German fluency with excellent grammar, syntax, and style.
  • Experience in German literary editing, copywriting, criticism, or NLP annotation.
  • Understanding of literary complexity, tone, and regional language variation.
  • Exceptional attention to detail, including spotting translation errors and unnatural phrasing.
  • Strong written communication and experience working with distributed teams.
  • Interest in AI, translation technology, or language-model research is preferred but not required.

Responsibilities

  • Evaluate German text quality in translation, summarization, dialogue, and grammar-correction use cases.
  • Edit, refine, and rewrite German content to meet linguistic, cultural, and literary standards.
  • Annotate German texts for grammatical, syntactic, semantic, and stylistic features.
  • Identify model errors and gaps; collaborate with researchers to improve German-language performance.
  • Provide detailed linguistic feedback to inform model training and evaluation.

Job description

SaidGig is seeking a native or near-native German linguist to evaluate and improve language models. You will review model-generated German content, edit for accuracy, and annotate texts to support research.

This remote, hourly engagement pays $50 per hour and offers hands-on experience with state-of-the-art AI development, contributing to translations, grammar, and stylistic improvements across diverse use cases.

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