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AI Training Data Specialist

Crossing Hurdles

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Original Job Summary

About the Role

The AI Training Data Specialist position at Crossing Hurdles connects you with leading AI research labs to help build and train cutting-edge AI models. As a Generalist Data Annotation Expert on an hourly contract basis, you will work approximately 20 hours per week, remotely, with flexible, asynchronous hours.

Role Responsibilities

  • Synthesize and interpret large volumes of diverse data.
  • Annotate and categorize text, images, and other data types using detailed guidelines and taxonomies.
  • Apply rubrics and standardization protocols to ensure consistent high-quality output.
  • Identify and flag inconsistencies and ambiguities in datasets.
  • Contribute to the enhancement and refinement of AI systems.

Requirements

  • Strong ability to organize and structure complex or large datasets.
  • Excellent written communication skills.
  • Experience with rubrics, taxonomies, or standardized annotation guidelines (preferred but not mandatory).
  • Detail-oriented and capable of working independently in a fully remote, asynchronous environment.

Application Process

Submit your resume for consideration and complete the Preference Selection Calibration Assessment as part of candidate calibration. Response time for application reviews is typically 3–5 business days.

Key Skills/Competency

  • Data Annotation
  • Data Synthesis
  • Standardization
  • Quality Assurance
  • Remote Work
  • Communication
  • Data Organization
  • Taxonomy Guidelines
  • Attention to Detail
  • Asynchronous Workflow

How to Get Hired at Crossing Hurdles

🎯 Tips for Getting Hired

  • Tailor your resume: Highlight data annotation experience and remote work skills.
  • Understand guidelines: Familiarize yourself with rubrics and taxonomies.
  • Research Crossing Hurdles: Explore their recruitment approach and AI focus.
  • Prepare examples: Showcase your work on complex datasets.

📝 Interview Preparation Advice

Technical Preparation

Review data annotation tools.
Study taxonomies and guidelines.
Practice dataset consistency checks.
Familiarize with asynchronous work tools.

Behavioral Questions

Describe your remote work experience.
Explain handling ambiguous datasets.
Share teamwork in asynchronous environments.
Discuss time management strategies.