Meridial

Meridial

Homography Specialist - Freelance AI Trainer Project

United States of America · Contract

Sponsorship not specifiedDetected 25 days ago
Computer VisionRoboticsAR/VRResearchCommunicationProblem Solving

About the role

  • Are you an experienced Homography specialist eager to shape the future of AI?
  • Large-scale language models and computer vision systems are evolving rapidly, moving beyond basic perception into advanced mathematical modeling, spatial reasoning, and applied problem-solving.
  • That training data begins with you-your expertise will help power the next generation of AI.

Responsibilities

  • With high-quality training data, tomorrow's AI can deliver more accurate, reliable, and innovative solutions across fields such as robotics, augmented reality, 3D reconstruction, and visual mapping.
  • Ready to channel your homography expertise into building the AI tools of tomorrow?
  • Apply today and help train the model that will support learners, researchers, and professionals worldwide.

Requirements

  • A degree in mathematics, computer science, or engineering is a plus.

Compensation

  • We offer a pay range of $8 to $65 per hour, with the exact rate determined after evaluating your experience, expertise, and geographic location.

Benefits

  • You'll work with cutting-edge AI tools, evaluate and refine outputs, and provide feedback on homography transformations, projective geometry, multi-view consistency, and real-world applications in vision and graphics.
  • Foundational knowledge of homography and projective geometry is required, supported by coursework, research, or applied experience in computer vision, robotics, graphics, or related fields.

Company info

  • We're looking for a Homography specialist who can bring mathematical precision, domain knowledge, and practical problem-solving skills to training data.
  • We offer a pay range of $8 to $65 per hour, with the exact rate determined after evaluating your experience, expertise, and geographic location.

This listing is sourced directly from Meridial's careers page and normalized into a canonical job model.