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Remote LLM Data Annotator - 32449

Turing

Engineering & Technology

Confidential
  • Minimum Qualification :

Job Description/Requirements



















Join a leading U.S.-based AI company as a Remote LLM Data Annotator, where you will help improve large language models (LLMs) by documenting real-world digital workflows. This role involves identifying realistic user scenarios, building prompts, and providing step-by-step descriptions of expected AI performance.


Job Responsibilities:

  • Train LLMs by annotating workflows across various digital tools like Microsoft Office, Google Suite, Dropbox, Notepad, Web Browsers, PDF Readers, Calculator, Slack, Zoom, Canva, and Adobe Photoshop.
  • Identify and document common user tasks, ensuring precise, structured, and easy-to-follow instructions.
  • Provide detailed prompts and expected AI responses for real-world digital workflows.


Sample tasks include:

  • Creating an Excel sheet with sales data, building a pivot table, and applying conditional formatting to highlight sales above $10,000.
  • Designing a 1080x1080px Instagram post in Canva with a "Flash Sale 50% Off" text, a bright background, and a company logo, then exporting it as a PNG.
  • Setting up a Google Form titled "Q1 Team Feedback Survey" with rating-scale and open-ended questions, sharing it in Slack, and pinning the message for easy access.


Job Requirements:

  • Strong English reading and writing comprehension.
  • Attention to detail and ability to document workflows accurately.
  • Pursuing or completed a Bachelor's degree in Engineering, Literature, Journalism, Communications, Arts, Statistics, or a related field.
  • A minimum 2-5 hour work overlap with PST (UTC-8:00) is required.


Perks:

  • Work on AI projects with top LLM companies.
  • Flexible working hours in a fully remote contractual role.
  • Salary range: $500 - $1000 per month, based on experience and expertise.
  • Gain hands-on experience in AI model training and workflow optimization.


Application Process:

  • Shortlisted candidates will receive an automated analytical challenge.
  • Upon successfully clearing the challenge, candidates will proceed to onboarding.












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