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Remote Data Annotator Charlotte
Remote / Online - Candidates ideally in
Charlotte, Mecklenburg County, North Carolina, 28277, USA
Listed on 2026-10-09
Charlotte, Mecklenburg County, North Carolina, 28277, USA
Listing for:
Rex.zone
Remote/Work from Home
position Listed on 2026-10-09
Job specializations:
-
IT/Tech
Data Annotation/ AI Labeling, AI Evaluation
Job Description & How to Apply Below
Join Rex.zone to create high-quality training data for real-world AI/ML and LLM training pipelines. You will label, rate, and review text, image, and multimodal content while following strict annotation guidelines and contributing to training data quality improvements.
Join Rex.zone to create high-quality training data for real-world AI/ML and LLM training pipelines. You will label, rate, and review text, image, and multimodal content while following strict annotation guidelines and contributing to training data quality improvements.
Key Responsibilities- Deliver accurate data annotation and data labeling across NLP and multimodal datasets
- Execute RLHF workflows including preference ranking, rubric-based scoring, and rationales
- Perform prompt evaluation and response grading for safety, factuality, and instruction-following
- Conduct QA evaluation by auditing samples, identifying recurring errors, and escalating guideline gaps
- Maintain annotation guidelines compliance and document edge cases clearly
- NLP labeling: classification, intent, sentiment, toxicity, summarization quality, named entity recognition
- LLM evaluation: prompt evaluation, RLHF preference ranking, helpfulness/truthfulness/safety rubric scoring
- Computer vision annotation (project-dependent): bounding boxes, polygons, attributes, image QA
- Content safety labeling: policy-based categorization for sensitive or regulated content
- Mid-Senior experience in data annotation, data labeling, QA evaluation, or AI data operations
- Strong written reasoning skills using rubrics and annotation guidelines
- Familiarity with RLHF concepts and large language model evaluation methods
- Comfort working independently in a remote, high-throughput environment
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