About The Position

At CloudFactory, we are a mission-driven team passionate about unlocking the disruptive potential of AI for the world. By combining advanced technology with a global network of talented experts, we make unusable data usable and inference reliable and trustworthy, driving real-world business value at scale. More than just a workplace, we're a global community founded on strong relationships and the belief that meaningful work transforms lives. Our commitment to earning, learning, and serving fuels everything we do, as we strive to connect one million people to meaningful work and build leaders worth following. At CloudFactory, we believe in building a workplace where everyone feels empowered, valued, and inspired to bring their authentic selves to work. We are: Mission-driven: we focus on creating economic and social impact. People-centric: we care deeply about our team's growth, well-being, and sense of belonging. Innovative: we embrace change and find better ways to do things, together. Globally connected: we foster collaboration between diverse cultures and perspectives. If you're ready to earn, learn, serve, and be part of a vibrant global community, CloudFactory is your place.

Requirements

  • Bachelor's degree or higher in linguistics, computational linguistics, or NLP (Master's or PhD a plus)
  • Demonstrated expertise in semantics and morphosyntax, ideally applied to corpus linguistics or NLP annotation work
  • Experience with language data annotation, corpus tagging, or AI/ML training data projects strongly preferred
  • Native or near-native English proficiency (CEFR C2)

Responsibilities

  • Disambiguate word- and phrase-level meaning to build high-quality training data for AI language models
  • Apply morphosyntactic and semantic analysis to tag and validate grammar concepts with precision
  • Distinguish read-aloud from spontaneous speech to support speech data annotation
  • Write grammar explanations that are linguistically rigorous and genuinely useful to learners
  • Design vocabulary tasks grounded in real usage patterns, not textbook cliches
  • Shape lesson outlines that reflect how English is actually used, informed by corpus and annotation data
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