Image Reasoning Generalist | $35/hr Remote
Overview
This remote role puts you at the heart of training advanced AI models by sourcing images that expose their blind spots. You'll focus specifically on counting tasks—finding visuals that trick AI into miscounting, then documenting exactly why. The work is fully remote with a straight $35/hour rate, and it's perfect for detail-obsessed thinkers who enjoy a mix of creative search and structured labeling. No prior AI experience is required, but a sharp eye for visual nuance is essential.
What You'll Do4
- 1Scour the web, your own files, or AI generators for images that could trip up an AI's counting abilities
- 2Pinpoint the specific source of confusion, such as misleading text, repeated objects, lookalike items, or ambiguous measurement units
- 3Test each image against AI models using a standard counting prompt and record the results
- 4Draft extra, more contextual prompts to guide the model, then annotate the task with clear labels
Requirements4
- 1Excellent written English to craft prompts that are unambiguous and easy for models to parse
- 2A meticulous eye for spotting tiny details in images and predicting what might confuse an AI system
- 3Consistency and reliability, since you'll process a high volume of tasks that demand careful judgment
- 4Bonus experience includes photography, image editing, data labeling, or similar detail-focused work
Who Should Apply
You're the kind of person who notices what others miss—whether it's a duplicate object hidden in a photo or an oddly placed caption that throws off interpretation. You enjoy repetitive but mentally engaging work, and you take pride in doing it accurately every time. If you've dabbled in photo editing, data annotation, or simply love puzzles, this role will feel like a natural fit.
Salary Insight
$35 per hour, paid on an hourly basis.
Location
Required Skills
Application Tip
Before applying, create 2–3 sample images that you think would trick an AI counter, and write a short explanation of why. Mention these in your application—it shows you already understand the core challenge.
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