Entry Level Data Labeling Specialist (United States, Remote)

Rex.zone; United States; On-site; USD 32,000 - 52,000; Full time

Job summary

Rex.zone is seeking detail-oriented data labeling professionals to join a team that labels and evaluates training data for AI/ML systems, including text, images, and multimodal tasks. You will follow strict guidelines, document rationales, and uphold throughput targets.

This role emphasizes accuracy, edge-case documentation, and willingness to learn new labeling tools and taxonomy updates as the project evolves.

Qualifications

• Comfort with detailed rules, annotation rubrics, and high-precision work.
• Strong written communication for documenting rationales and disagreements.
• Ability to learn new labeling tools and taxonomy updates quickly.
• Familiarity with annotation concepts (NER, CV annotation, RLHF) is helpful.

Responsibilities

• Perform data labeling and data annotation across NLP, computer vision, and multimodal datasets
• Apply annotation guidelines and document edge cases for adjudication
• Execute QA evaluation workflows (spot checks, consensus review, error categorization)
• Support RLHF by rating responses, ranking candidates, and flagging hallucinations or policy risks
• Conduct prompt evaluation and evaluation set curation to improve model performance
• Maintain throughput and accuracy targets while protecting sensitive data

Skills

Attention to detail

Precise annotation

Strong written communication

Tool learning agility

Job description

About Rex.zone

Rex.zone connects remote contributors with AI data operations work, including data labeling, RLHF evaluation, prompt evaluation, and quality assurance for NLP, computer vision, and large language model training pipelines.

Role Summary

You will label and evaluate training data that improves AI/ML systems, focusing on consistent guideline adherence and measurable training data quality. Work includes text, images, and multimodal evaluation tasks, plus structured QA workflows.

Key Responsibilities

• Perform data labeling and data annotation across NLP, computer vision, and multimodal datasets
• Apply annotation guidelines and document edge cases for adjudication
• Execute QA evaluation workflows (spot checks, consensus review, error categorization)
• Support RLHF by rating responses, ranking candidates, and flagging hallucinations or policy risks
• Conduct prompt evaluation and evaluation set curation to improve model performance
• Maintain throughput and accuracy targets while protecting sensitive data

What You Will Work On

• LLM evaluation: helpfulness, harmlessness, honesty, instruction following
• Content safety labeling: policy-based classification, severity rating, escalation signals
• NLP labeling: named entity recognition (NER), topic tagging, intent detection, factuality checks
• Computer vision annotation: bounding boxes, polygons, segmentation masks, image QA

Qualifications

• Comfort with detailed rules, annotation rubrics, and high-precision work
• Strong written communication for documenting rationales and disagreements
• Ability to learn new labeling tools and taxonomy updates quickly
• Familiarity with annotation concepts (NER, CV annotation, RLHF) is helpful but not required