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# AI Trainer for Video Annotation

- **Company:** Mercor, Inc.
- **Location:** Seattle, WA, United States (Remote available)
- **Salary:** $83,200.0 - $166,400.0
- **Contract:** Permanent contract
- **Skills:** Training Data, Artificial Intelligence
- **Published:** July 15, 2026
- **Apply:** https://www.careerjet.com/job/register/us989962c5880bdaf112915b0031683489

## About the Role

* Proficiency in English, including the ability to comprehend and execute complex written instructions.
 * Strong attention to detail and commitment to producing high-quality, consistent results.
 * Familiarity with video annotation tools or similar data labeling technologies.
 * Demonstrated ability to manage time effectively across repetitive or high-volume tasks.
 * Experience in AI training, content moderation, or quality assurance is beneficial.
 * Excellent written and verbal communication skills, especially in remote collaboration environments.
 * Comfortable adapting to evolving project priorities and embracing feedback for continuous improvement.

## Description

Join a dynamic project focused on advancing AI capabilities as an AI Trainer. In this role, you will leverage your domain expertise to train next-generation AI systems, shaping how models learn, reason, and perform through high-quality, real-world input. This opportunity involves annotating short video clips to create precise datasets for AI training, with no prior experience in AI required, your subject matter knowledge is what truly matters. Key Responsibilities

 * Review and annotate short video clips based on project-specific guidelines and objectives.
 * Apply prompt engineering strategies to ensure high-quality training data for AI models.
 * Utilize advanced timestamping to accurately mark relevant events or frames within video content.
 * Maintain consistency and attention to detail across large volumes of video-based tasks.
 * Collaborate remotely with fellow contributors and project coordinators to clarify requirements and resolve ambiguities.
 * Conduct self-driven quality assurance checks to verify the accuracy and completeness of submitted annotations.
 * Adhere to established processes for timely and reliable submission of deliverables.


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