Remote English Linguistics Evaluator for AI Research

Zekai

United States

Remote

USD 55.000 - 96.000

Teilzeit

Vor 9 Tagen

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Zusammenfassung

Zekai is seeking experienced English Linguistic Experts for a remote contract on an AI research project. The role focuses on evaluating LLM English tasks and curating high-quality linguistic data to improve model understanding and generation.

You will assess text quality across use cases, edit for linguistic and cultural accuracy, and annotate data to support ongoing model training. This is a 100% remote, US-located opportunity with distributed team collaboration.

Qualifikationen

  • Native or near-native fluency in English with strong grammar and style.
  • Experience in translation, editing, or linguistic annotation for NLP data.
  • Knowledge of linguistic nuances and context-appropriate usage.
  • Interest in AI and LLM-driven language technologies.
  • Attention to detail and ability to spot subtle errors.
  • Experience collaborating with distributed, remote teams.

Aufgaben

  • Evaluate the quality and accuracy of LLM-generated English text across translation, summarization, dialogue, and grammar tasks.
  • Edit, refine, or rewrite English content to meet linguistic and cultural standards.
  • Annotate English text for grammatical, syntactic, and semantic features.
  • Identify model errors and language gaps, and collaborate with AI researchers to improve LLM performance.
  • Provide detailed linguistic feedback to guide ongoing model training.

Kenntnisse

English proficiency
Linguistic annotation
NLP data work
Editing/translation

Jobbeschreibung

Zekai is seeking experienced English Linguistic Experts for a remote contract on an AI research project. The role focuses on evaluating LLM English tasks and curating high-quality linguistic data to improve model understanding and generation.

You will assess text quality across use cases, edit for linguistic and cultural accuracy, and annotate data to support ongoing model training. This is a 100% remote, US-located opportunity with distributed team collaboration.

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