> Markdown version of [/jobs/ext/4686899-ai-data-annotation-specialist](https://www.wearedevelopers.com/jobs/ext/4686899-ai-data-annotation-specialist). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI Data Annotation Specialist - **Company:** Everforth Apex - **Location:** Boca Raton, FL, United States - **Salary:** $49,920 - $58,240 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Spreadsheets, Machine Learning, Ontology Design - **Published:** November 12, 2026 - **Apply:** https://www.dice.com/job-detail/1ee7060e-d4d7-475e-85ee-522c68699c23 ## About the Role We are seeking a detail-oriented AI Data Annotation Specialist to support a high-impact AI fine-tuning initiative. This role is responsible for labeling, classifying, and validating datasets to improve the accuracy and performance of machine learning models. The ideal candidate combines precision, consistency, and the ability to follow structured guidelines in a fast-paced environment., * Experience in data annotation and labeling. * Strong attention to detail and ability to follow structured instructions. * Analytical thinking with the ability to interpret nuanced scenarios. * Familiarity with annotation tools (e.g., Labelbox, CVAT, Scale AI, or similar). * Proficiency with spreadsheets or data tracking tools. * Ability to work independently and manage high-volume tasks with consistency. Preferred Qualifications * Experience supporting machine learning or AI fine-tuning projects. * Exposure to taxonomy design, classification frameworks, or metadata structuring. ## Description * Perform data labeling and annotation across multiple data types (e.g., text, image, sensor data, behavioral signals). * Apply predefined guidelines to ensure high-quality, consistent annotations. * Review and validate labeled data to maintain accuracy and reduce bias. * Collaborate with AI/ML teams to refine labeling schemas and edge cases. * Identify anomalies, ambiguities, or gaps in datasets and escalate as needed. * Maintain documentation of labeling decisions and updates to taxonomy.