Remote Data Labeling Jobs UK

Remote data labeling jobs UK at Rex.zone focus on training-data creation and evaluation for AI/ML systems, including RLHF, LLM response rating, QA evaluation, computer vision annotation, and NLP labeling tasks like named entity recognition. You will follow annotation guidelines, validate training data quality, and help drive model performance improvement across large language model evaluation and content safety labeling workflows used by AI labs, tech startups, and annotation vendors. This full-time remote role supports production-grade LLM training pipelines with measurable quality targets, consistent rubric use, and audit-ready documentation. Explore and apply via Rex.zone to join ongoing labeling and evaluation programs.

Job Overview

About the Role

You will deliver high-precision data labeling and evaluation outputs used to train and validate machine learning models. The work spans NLP labeling (classification, named entity recognition, intent tagging), LLM evaluation (preference ranking, rubric-based scoring, prompt evaluation, RLHF signals), and computer vision annotation (bounding boxes, polygons, keypoints) as needed by active projects. You will apply annotation guidelines compliance, maintain training data quality, and produce clear rationales that improve inter-annotator agreement and model behavior.

What You Will Do

Core responsibilities include: (1) Label and review text, image, and multi-modal datasets using project rubrics and tooling; (2) Perform QA evaluation on peer outputs, escalate edge cases, and propose guideline clarifications; (3) Execute prompt evaluation and LLM response rating to generate RLHF-ready preference data; (4) Conduct content safety labeling for policy categories such as harassment, self-harm, violence, adult content, and regulated goods; (5) Track errors, maintain audit trails, and support calibration sessions to improve annotation consistency; (6) Collaborate with operations and engineering partners to refine task instructions and reduce ambiguity.

Workflows and Task Types

Typical workflows include: dataset onboarding, rubric training, gold-set calibration, production labeling, secondary QA review, and discrepancy resolution. Task types may include: sentiment and topic classification, named entity recognition, summarization evaluation, instruction-following checks, groundedness and factuality assessment, toxicity and policy compliance review, and computer vision annotation for detection and segmentation. Deliverables are measured by accuracy, consistency, throughput, and adherence to annotation guidelines.

Required Qualifications

You should have: (1) Experience delivering professional data labeling or QA evaluation work with documented quality metrics; (2) Strong reading comprehension and ability to apply complex rubrics consistently; (3) Familiarity with LLM evaluation concepts such as preference ranking, rubric scoring, and prompt evaluation; (4) Comfort working with annotation tools, structured forms, and versioned guidelines; (5) Ability to write concise rationales and capture edge cases for guideline improvement.

Preferred Qualifications

Nice to have: (1) Prior exposure to RLHF pipelines and large language model evaluation; (2) Computer vision annotation experience (boxes, polygons, keypoints); (3) NLP labeling experience including named entity recognition; (4) Experience with content safety labeling, trust & safety workflows, or policy taxonomies; (5) Experience working with AI labs, annotation vendors, BPOs, or tech startups on production datasets.

Quality and Performance Expectations

Success is defined by training data quality and reliable annotation guidelines compliance. You will be expected to meet accuracy and consistency targets, participate in calibration to improve inter-annotator agreement, respond to QA feedback, and document decisions for difficult cases. You will help reduce label noise, improve model performance improvement outcomes, and maintain stable labeling throughput without sacrificing correctness.

Compensation and Employment Details

This is a FULL_TIME Remote role. Salary range is 63360 to 126720 USD per YEAR, depending on scope, performance, and project needs. Remote roles remain explicitly Remote, and work is delivered through Rex.zone program operations with standardized onboarding, training, and QA processes.

How to Apply on Rex.zone

Apply through Rex.zone by completing your profile, selecting relevant data labeling and LLM evaluation task preferences, and submitting any requested work samples. Shortlisted candidates may complete calibration tasks to verify rubric understanding, annotation guidelines compliance, and QA evaluation consistency.

Frequently Asked Questions

Q: What are remote data labeling jobs UK on Rex.zone?
They are remote roles focused on creating and evaluating training data for AI/ML systems. Work may include data labeling, RLHF-style preference judgments, LLM evaluation, prompt evaluation, QA evaluation, named entity recognition, computer vision annotation, and content safety labeling depending on the project.
Q: Is this role Remote and full-time?
Yes. The Remote Type is Remote and the Employment Type is FULL_TIME. The workflows are designed for distributed teams with standardized guidelines, calibration, and QA review.
Q: What skills are most important for this job keyword?
Data labeling, data annotation, RLHF, LLM evaluation, prompt evaluation, QA evaluation, named entity recognition, computer vision annotation, and content safety labeling. Strong rubric adherence and training data quality discipline are essential.
Q: What types of AI models does this work support?
The work supports large language model evaluation and training pipelines, as well as classic ML and computer vision models. Outputs are used to improve model behavior, reduce hallucinations, and increase policy compliance and safety.
Q: Do I need previous experience with RLHF?
RLHF experience is helpful but not always required. You must be able to follow evaluation rubrics, produce consistent preference rankings, and incorporate QA feedback—these are the core skills that translate into RLHF-ready data.
Q: How is quality measured in data labeling and evaluation?
Quality is measured through accuracy against gold standards, inter-annotator agreement, QA evaluation pass rates, error severity trends, and auditability of rationales. Consistent annotation guidelines compliance is a primary requirement.
Q: What employers use this kind of labeled data?
AI labs, tech startups, annotation vendors, and BPOs commonly use labeled datasets and evaluation outputs to train and validate models across NLP, computer vision, and content safety domains.
Q: What is the salary range and pay period?
Salary Currency is USD, Salary Min is 63360, Salary Max is 126720, and Pay Period is YEAR.

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