Remote Data Labeling Jobs London

Rex.zone; United States; Remote; USD 21,000 - 34,000; Full time

Job summary

Rex.zone in London is seeking remote data labeling professionals to support AI/ML training workflows, including data labeling, RLHF ranking, prompt evaluation, and QA evaluation for language models and computer vision systems.

As a remote team member, you will deliver accurate labeling for text, image, and multimodal tasks, follow strict guidelines, document edge cases, and participate in peer reviews to improve data quality.

Qualifications

• Mid-senior experience in data labeling/data annotation or QA evaluation for AI/ML datasets.
• Strong written English and consistent guideline adherence.
• Experience with RLHF, prompt evaluation, or rubric-based grading preferred.
• Comfort working remotely with asynchronous collaboration and documentation.

Responsibilities

• Deliver accurate data labeling and data annotation for text, image, and multimodal tasks.
• Perform RLHF ranking and comparative evaluations using structured rubrics.
• Complete prompt evaluation and QA evaluation to validate guideline compliance and consistency.
• Flag ambiguous cases, document edge cases, and propose guideline clarifications.
• Run self-checks and peer reviews to improve training data quality and reduce defects.
• Handle sensitive content safely during content safety labeling.
• Track throughput and quality metrics (accuracy, agreement rate, defect rate) and act on feedback.

Skills

Data labeling

English proficiency

RLHF/prompt evaluation

Remote collaboration

Job description

About The Role

Remote Data Labeling Jobs London at Rex.zone connect skilled annotators with real AI/ML training workflows including data labeling, RLHF preference ranking, prompt evaluation, and QA evaluation for large language models and computer vision systems.

About The Role

Remote Data Labeling Jobs London at Rex.zone connect skilled annotators with real AI/ML training workflows including data labeling, RLHF preference ranking, prompt evaluation, and QA evaluation for large language models and computer vision systems.

Key Responsibilities

• Deliver accurate data labeling and data annotation for text, image, and multimodal tasks
• Perform RLHF ranking and comparative evaluations using structured rubrics
• Complete prompt evaluation and QA evaluation to validate guideline compliance and consistency
• Flag ambiguous cases, document edge cases, and propose guideline clarifications
• Run self-checks and peer reviews to improve training data quality and reduce defects
• Handle sensitive content safely during content safety labeling
• Track throughput and quality metrics (accuracy, agreement rate, defect rate) and act on feedback

Projects You May Work On

• NLP labeling (classification, sentiment, summarization quality)
• Named entity recognition (NER) and entity linking
• Content safety labeling (toxicity, self-harm, adult content)
• Computer vision annotation (bounding boxes, polygons, keypoints, segmentation)
• Multimodal QA evaluation where images and text must align

Required Qualifications

• Mid-senior experience in data labeling/data annotation or QA evaluation for AI/ML datasets
• Strong written English and consistent guideline adherence
• Experience with RLHF, prompt evaluation, or rubric-based grading preferred
• Comfort working remotely with asynchronous collaboration and documentation