Drone And Maritime Imagery Annotation Specialist

You will improve AI training data by reviewing machine-generated annotations in drone and maritime imagery. The work includes still images and may expand to video frames as the project develops.

You will work in a dedicated labeling application within a secure government environment. Accuracy, consistency, dependable production, and clear communication about unclear images or guidelines are important.

• Review and correct machine-generated bounding boxes.
• Add labels for vehicles, boats, people, aircraft, and other objects missed by AI systems.
• Annotate both daylight and thermal sensor imagery.
• Record ambiguous cases and communicate guideline questions clearly.
• Maintain reliable throughput while meeting accuracy and consistency expectations.
• Move into video-frame annotation if the project scope expands.

What It Pays And Takes

This is a remote, part-time contractor role for candidates in the United States. Prior AI experience is not required, but careful visual judgment and the ability to follow detailed guidelines are important.

• Pay: $20 to $60 per hour.
• Work arrangement: Remote contractor role.
• Schedule: Part-time.
• Location: Open to candidates in the United States.
• Language: Fluency in English.
• Strong attention to detail for repetitive, accuracy-critical work.
• Clear written and verbal communication.
• Comfort using dedicated annotation software and learning new tools.
• Helpful experience includes data labeling, bounding-box annotation, image annotation, robotics data, autonomous-system datasets, or other quality-controlled annotation work.
• Familiarity with aerial, maritime, daylight, or thermal imagery is valuable.

How It Works

Apply on OpenTrain with your resume, then complete the application on the hiring site.

About AI Training Work

AI training work is the human side of building artificial intelligence: people label images, review model outputs, and prepare examples that help AI systems learn. Careful contributors are needed because accurate, consistent annotations help models recognize objects and interpret real-world data.