Meridial

Meridial

Drone Systems Specialist – Freelance AI Trainer Project

United States of America · Contract

Sponsorship not specifiedDetected 25 days ago
Computer VisionRobotics

About the role

  • This opportunity focuses on the careful interpretation, segmentation, and annotation of drone imagery, with an emphasis on isolating structurally meaningful components of a wide range of unmanned aerial vehicles.

Responsibilities

  • On a typical day, you will evaluate diverse drone images and apply strict annotation standards designed to optimize downstream model performance.

Requirements

  • The ideal candidate combines a strong technical understanding of drone architecture with an ability to follow nuanced visual-annotation protocols applied consistently across large datasets.
  • A central requirement is the ability to isolate the core structure of a drone while excluding peripheral or transient elements.
  • Experience with professional or academic work involving drones, robotics imagery, aerial systems, or technical visual datasets is strongly advantageous.

Compensation

  • We offer a pay range of $6 to $65 per hour, with the exact rate determined after evaluating your experience, expertise, and geographic location.

Benefits

  • We are seeking a highly skilled specialist with deep experience in drone systems, drone morphology, or related technical imaging domains to support the development of high-precision computer vision models.
  • A subtle but essential part of this role is learning to distinguish between structural and non-structural features, especially across varied drone designs, lighting conditions, and environments.
  • As a contractor, you will supply a secure computer and high-speed internet; company-sponsored benefits such as health insurance and PTO do not apply.

Company info

  • We offer a pay range of $6 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.