Remote Data Annotator Jobs Manchester

Rex.zone; California (MO); Remote; USD 42,000 - 66,000; Full time

Job summary

Rex.zone offers remote, full-time data annotation work supporting AI/ML and LLM training pipelines. You will create high-quality labeled datasets and human feedback signals, including RLHF ranking, prompt evaluation, and QA evaluation, while maintaining strict annotation guidelines compliance and training data quality.

You will adapt to asynchronous review cycles, document decisions thoroughly, and contribute to ongoing improvements in annotation quality and model performance.

Qualifications

• Experience with structured labeling workflows and web-based annotation tools.
• Strong written reasoning and ability to follow detailed guidelines with high precision.
• Comfort working asynchronously with reviewer feedback loops and QA processes.

Responsibilities

• Label and categorize text, image, and multimodal data using defined taxonomies and policies.
• Perform RLHF pairwise preference ranking and write clear rationales/justifications.
• Execute prompt evaluation and response quality scoring for LLM evaluation.
• Run QA evaluation audits, calibration tasks, and gold-set checks to improve consistency.
• Complete named entity recognition (NER) and span labeling for NLP datasets.
• Support content safety labeling and sensitive content handling per policy.
• Maintain throughput and accuracy targets with detailed decision logs for reviewer traceability.

Skills

Structured labeling workflows

Web-based annotation tools

Attention to detail

Strong written reasoning

Tools

Annotation tools

Job description

About The Role

Rex.zone connects annotators with remote, full-time data annotation work supporting AI/ML and LLM training pipelines. You will create high-quality labeled datasets and human feedback signals, including RLHF preference ranking, prompt evaluation, and QA evaluation, while maintaining strict annotation guidelines compliance and training data quality.

About The Role

Rex.zone connects annotators with remote, full-time data annotation work supporting AI/ML and LLM training pipelines. You will create high-quality labeled datasets and human feedback signals, including RLHF preference ranking, prompt evaluation, and QA evaluation, while maintaining strict annotation guidelines compliance and training data quality.

What You Will Do

• Label and categorize text, image, and multimodal data using defined taxonomies and policies
• Perform RLHF pairwise preference ranking and write clear rationales/justifications
• Execute prompt evaluation and response quality scoring for LLM evaluation
• Run QA evaluation audits, calibration tasks, and gold-set checks to improve consistency
• Complete named entity recognition (NER) and span labeling for NLP datasets
• Support content safety labeling and sensitive content handling per policy
• Maintain throughput and accuracy targets with detailed decision logs for reviewer traceability

Requirements

• Experience with structured labeling workflows and web-based annotation tools
• Strong written reasoning and ability to follow detailed guidelines with high precision
• Comfort working asynchronously with reviewer feedback loops and QA processes
• Preferred: RLHF, prompt evaluation, LLM evaluation, NER, or computer vision annotation exposure

Quality & Performance

You will be evaluated on guideline adherence, inter-annotator agreement, edge-case handling, and overall training data quality. Expect structured QA cycles, periodic calibration, and reviewer feedback to support measurable model performance improvement.