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AI Data Training Generalist

AI Data Training Generalist (Multiple Openings)

We are building a pipeline of talented individuals for a range of AI data training roles. This is a general application designed to identify candidates for current and future opportunities across multiple projects. If you are interested in contributing to cutting-edge AI systems and enjoy working with data, this role is for you.

About the Role
As an AI Data Training Generalist, you will support the development and improvement of AI models by creating, labeling, reviewing, and refining data. This is a flexible, multi-functional role that may involve a variety of tasks depending on project needs.

Important Note
This is a pipeline-building job post. By applying, you are expressing interest in multiple potential openings. Candidates will be evaluated and matched to relevant projects as opportunities become available. You must apply through our official website to be considered.

Key Responsibilities

  • Annotate, label, create, or review datasets used to train AI systems
  • Evaluate AI-generated outputs for accuracy, relevance, and quality
  • Follow detailed guidelines to ensure consistency and precision
  • Provide feedback to improve model performance
  • Adapt to different task types across text, image, audio, or multimodal data

Qualifications

  • Strong attention to detail and analytical thinking
  • Excellent written and/or verbal communication skills
  • Ability to follow complex instructions and guidelines
  • Comfort working independently in a remote or distributed environment
  • Prior experience in data annotation, research, content review, or related fields is a plus

What We Offer

  • Opportunity to work on cutting-edge AI projects
  • Flexible, project-based work environment
  • Exposure to a variety of domains and data types
  • Ongoing opportunities as new projects become available

 

How to Apply
 

To be considered, you must submit your application through our official website. Applications submitted elsewhere will not be reviewed.