Imagery Analyst - AI/ML Training Data

$20–60/hr

rate as listed

Workload: ~15h/week
Engagement: Project-based
Location: Location not stated
Field: STEM and Coding

Review drone and maritime sensor imagery, correct machine-generated bounding boxes, and annotate missed vehicles, boats, people, and aircraft. The work covers daylight and thermal data in a secure Navy environment, with image-based submissions and a possible transition to video frames.

The work

You will inspect still imagery from drone and maritime sensors, refine machine-generated bounding boxes, and add annotations for objects the model missed. Day-to-day tasks include labeling vehicles, boats, people, and aircraft in daylight and thermal imagery, applying project guidelines consistently, and documenting ambiguous cases rather than resolving them by guesswork. The project may later expand to video-frame annotation.

What the screen is likely to assess

The screening will focus on verifiable experience with bounding boxes, data labeling, aerial or maritime imagery, thermal sensors, or other accuracy-critical visual review. Expect follow-up questions about how you handle partial occlusion, uncertain object classes, inconsistent model outputs, and quality control during repetitive, high-volume work. Prior AI experience is not required, but familiarity with robotics or autonomous-system labeling is a plus.

Logistics

This is a remote, US-based contractor role that may be part-time or full-time. Work is submitted per completed image through micro1’s proprietary labeling application in a secure Navy environment, and compensation is based on image submissions. The listing does not specify a fixed schedule, typical weekly hours, or whether all work can be completed asynchronously, so confirm those details during onboarding. U.S. citizenship is mandatory, and candidates must be able to obtain a DoD Common Access Card or be willing to engage in the Navy sponsorship process.

Pay band: $20–60/hr

What they look for

Data labeling
Data annotation
Labeling accuracy
Attention to detail

Included with this listing

Interview prep — 10 questions

Matched to what this platform's screening actually measures. Answer each out loud in 60–90 seconds: claim → concrete example → implication.

What it tests: This tests whether your imagery experience is directly relevant and whether you can give verifiable details about sensor type, object classes, annotation format, volume, and quality expectations.
A strong answer: Describe a specific project, the source imagery, the objects or events you identified, the labeling tool or output format, and how quality was measured. If your experience is adjacent rather than exact, explain the transferable visual-analysis demands without presenting it as maritime or drone work.
Where people lose it: Candidates lose points by saying they have worked with imagery without distinguishing ordinary photographs from aerial, maritime, infrared, or other sensor data.

Summary, eligibility guidance and screening preparation are prepared by Sidequest from the platform's posting. Check the source listing before applying.

STEM AND CODING on Sidequest

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