JHONTISE CARTER
I’m Jhontise Carter, a scientific AI evaluator and STEM-focused data annotation professional with a B.S. in Biology from Texas State University.
About
I’m Jhontise Carter, a scientific AI evaluator and STEM-focused data annotation professional with a B.S. in Biology from Texas State University. I review complex AI outputs with a careful, evidence-based approach—checking factual accuracy, relevance, completeness, consistency, and the quality of reasoning to ensure answers are truly defensible.
I enjoy turning complicated scientific tasks into clear evaluation criteria and improving model performance through structured rubrics, hallucination detection, and detailed feedback. With experience across remote evaluation and quality assurance environments, I’m comfortable working independently and interpreting technical and visual information (including quantitative and scientific figure-style evidence) to help build more reliable AI systems.
- 6 years' experience
- Joined November 2026
Industry experience
- Life Sciences
- Education
- Professional Services
More experts in San Marcos, United States
Work experience
Evaluated digital content using detailed project guidelines and structured quality rubrics. Assessed information within broader context, researched and cross-checked when needed, and flagged inconsistencies, misleading information, and classification issues. Documented evaluation outcomes accurately while maintaining strict attention to detail across text and visual/contextual signals. Adapted to changing instructions and met productivity and accuracy expectations in an independent remote environment.
Evaluated large language model responses for factual accuracy, logical consistency, relevance, completeness, and adherence to task instructions. Identified hallucinations, unsupported claims, contradictions, missing information, and reasoning failures. Developed and rewrote responses to improve clarity and accuracy while preserving intent, and provided feedback on why outputs were correct or incorrect using structured rubrics. Compared multiple model answers to select the strongest option and performed quality checks across high volumes of assignments in remote settings.
Education
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