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AI Terminology Consistency Enforcement Engineer

Marcus Sokolov

Full-time · Job · Berlin

About the role

Terminology inconsistency in localization is expensive. A software product that uses 'dashboard' in the English interface but has three different translations of 'dashboard' across 12 language markets — 'Übersicht', 'Dashboard', and 'Instrumententafel' all appearing in German depending on when the string was translated — creates a fragmented user experience that erodes trust and increases support burden. We provide localization infrastructure for 70 software and media companies. Terminology consistency — ensuring that approved glossary terms are used consistently across all translated strings, in all languages, across all content updates — is one of the most labor-intensive quality assurance tasks in our workflow. Human terminologists review a fraction of the content. The rest goes unchecked. You'll build the automated terminology consistency enforcement system: detecting approved glossary violations in translated content, identifying cases where multiple translations of the same source term are used inconsistently, and integrating enforcement checks into the translation workflow so violations are caught before delivery rather than discovered by end users. The model needs to handle 28 language pairs across diverse content types: UI strings, marketing copy, support documentation, and subtitle tracks.

Responsibilities

  • Build terminology consistency detection model identifying glossary violations in translated content
  • Implement cross-string consistency checking detecting multiple translations of the same source term
  • Design 28-language-pair enforcement pipeline with language-specific glossary models
  • Integrate consistency checks into translation workflow APIs triggering pre-delivery review
  • Create violation severity scoring prioritizing critical terminology errors for immediate correction
  • Build glossary violation reporting providing translators with specific correction guidance

Requirements

  • 3+ years in NLP engineering with multilingual text processing or localization experience
  • Strong Python and HuggingFace skills with multilingual model experience
  • Understanding of translation workflows, glossary management, and terminology standards
  • Experience building text comparison or consistency checking systems
  • Familiarity with software localization formats and content types

Benefits

  • Fully remote across EU time zones
  • 30 days PTO plus local holidays
  • Private health insurance
  • €2,000 annual learning budget
  • Equity participation

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Categories

NLP & Prompt Engineering Automation & AI Workflows

Skills

python pytorch hugging face sql fastapi

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Level

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Salary Range

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Skills

python pytorch hugging face sql fastapi pandas

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