Remote AI Data Annotator for NLP, Vision & RLHF

Rex.zone; Miami (FL); On-site; USD 41,328 - 68,880; Full time

Job summary

Rex.zone is hiring a full-time, fully remote AI Data Annotation Specialist to support training data creation for modern AI/ML systems. You will label and review text, images, audio, and video to improve LLM evaluation, RLHF feedback quality, and computer vision annotation accuracy.

The role emphasizes strong annotation guidelines compliance, training data quality, and the ability to justify labeling decisions with clear rationale, while thriving in a remote, metrics-driven environment.

Qualifications

• Mid-Senior experience in AI data annotation, data labeling, or QA evaluation/data operations.
• Familiarity with LLM evaluation concepts, RLHF workflows, and rubric-based scoring.
• Strong written communication with ability to justify labeling decisions with clear rationale.
• High attention to detail and adherence to annotation guidelines.
• Comfort working remotely in a metrics-driven production environment with iterative feedback.

Responsibilities

• Annotate and review text datasets for NLP tasks such as classification and named entity recognition.
• Perform RLHF tasks including preference ranking and rubric-based scoring.
• Conduct prompt evaluation and QA evaluation to identify edge cases, label noise, and guideline gaps.
• Label computer vision data (bounding boxes, polygons, keypoints, segmentation masks) and validate accuracy.
• Apply content safety labeling and policy-based moderation labels for sensitive content categories.
• Document decisions, propose guideline improvements, and support calibration sessions.
• Track quality metrics (disagreement rates, audit pass rates) and contribute to data quality initiatives.

Skills

AI data annotation

RLHF workflows

NLP data labeling

Written communication

Remote work

Job description

Rex.zone is hiring a full-time, fully remote AI Data Annotation Specialist to support training data creation for modern AI/ML systems. You will label and review text, images, audio, and video to improve LLM evaluation, RLHF feedback quality, and computer vision annotation accuracy.

The role emphasizes strong annotation guidelines compliance, training data quality, and the ability to justify labeling decisions with clear rationale, while thriving in a remote, metrics-driven environment.