Remote Data Labeling Jobs in Manchester

Rex.zone is hiring for remote data labeling jobs in Manchester focused on building high-quality training data for AI systems. As a data labeling professional, you will apply annotation guidelines to create and validate datasets used in NLP and computer vision, including RLHF preference ranking, prompt evaluation, content safety labeling, and QA evaluation. Your work directly improves training data quality, model performance improvement, and large language model evaluation outcomes across real production ML training pipelines. Explore full-time remote roles supporting AI labs, tech startups, and annotation vendors through consistent labeling, review workflows, and measurement-driven quality assurance.

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Remote Data Labeling Jobs in Manchester

Title: Remote Data Labeling Specialist (Manchester) Date: 25-02-2026 Company: Rex.zone Country: US Remote Type: Remote Employment Type: FULL_TIME Experience Level: Mid-Senior Industry: Technology Job Function: Engineering Skills: Data labeling, Data annotation, RLHF, Prompt evaluation, QA evaluation, Named entity recognition, Computer vision annotation, Content safety labeling, Annotation guidelines compliance, Training data quality Salary Currency: USD Salary Min: 63360 Salary Max: 126720 Pay Period: YEAR

About the Role

You will deliver production-grade labeled data for AI/ML training workflows, collaborating with reviewers and project leads to meet throughput and accuracy targets. Typical tasks include text classification, entity and relation labeling, instruction/prompt evaluation, RLHF preference ranking, and image/video bounding boxes or segmentation for computer vision. You will follow annotation guidelines compliance requirements, document edge cases, and contribute to continuous improvements that reduce ambiguity and increase inter-annotator agreement.

What You Will Do

["Label and review text and multimodal data for NLP and computer vision annotation tasks","Perform RLHF ranking and QA evaluation to support large language model evaluation","Execute prompt evaluation for instruction-following, truthfulness, and helpfulness signals","Apply content safety labeling policies (hate, harassment, self-harm, sexual content) with consistent rationale","Track training data quality metrics (accuracy, precision/recall where applicable, agreement rates) and escalate guideline gaps","Maintain clear audit trails: decision notes, disagreement reasons, and edge-case documentation","Work in secure environments and follow data handling and confidentiality requirements"]

Required Qualifications

["Mid-Senior experience in data labeling, data annotation, QA evaluation, or related data operations roles","Strong attention to detail and ability to follow complex annotation guidelines compliance","Experience with NER (named entity recognition), classification taxonomies, or structured labeling schemas","Comfort working with ambiguous language and edge cases in LLM training pipelines","Ability to communicate labeling rationale clearly and consistently for reviewer alignment","Stable internet connection and ability to work effectively in a fully Remote setup"]

Preferred Qualifications

["Hands-on exposure to RLHF workflows (pairwise preference ranking, rubric-based scoring)","Experience labeling for computer vision annotation (bounding boxes, polygons, keypoints, segmentation)","Familiarity with content safety labeling standards and policy-driven enforcement","Experience with prompt evaluation for instruction tuning and model behavior measurement","Understanding of dataset versioning, sampling for audit, and feedback loops for model performance improvement"]

Tools and Workflow

["Annotation platforms and labeling tools used by AI labs, tech startups, BPOs, and annotation vendors","Quality assurance processes: gold sets, consensus review, adjudication, and calibration sessions","Operational best practices: batch management, time-on-task tracking, and defect categorization","Secure data handling and controlled access procedures"]

Compensation and Employment Details

["Salary Currency: USD","Salary Range: 63360 to 126720 per year","Employment Type: FULL_TIME","Remote Type: Remote","Experience Level: Mid-Senior","Location Targeting: Manchester (Remote)"]

How to Apply on Rex.zone

["Visit Rex.zone and open the Remote Data Labeling Jobs in Manchester listing","Submit your profile with relevant data labeling, RLHF, and QA evaluation experience","Complete any skills screening related to annotation guidelines compliance and training data quality","Start remote onboarding once matched to a project with the right domain (NLP, computer vision, or content safety)"]

Search Modifiers Covered

["remote","full-time","contract","freelance","entry-level","senior","NLP","computer vision","content safety","LLM training","AI labs","tech startups","BPOs","annotation vendors"]

Frequently Asked Questions

  • Q: What are remote data labeling jobs in Manchester?

    These are Remote roles targeted to Manchester candidates where you create and review labeled datasets used to train and evaluate AI models. Work can include data labeling for NLP, computer vision annotation, content safety labeling, and large language model evaluation through RLHF and prompt evaluation.

  • Q: Is this role fully Remote?

    Yes. The Remote Type is Remote and the workflows are designed for distributed teams, with secure access and standardized QA evaluation processes.

  • Q: What does RLHF involve in data labeling jobs?

    RLHF (Reinforcement Learning from Human Feedback) often includes preference ranking between model outputs, rubric-based scoring, and justification notes that help improve model behavior and training data quality.

  • Q: What skills are most important for this job?

    Key skills include data labeling, data annotation, annotation guidelines compliance, QA evaluation, named entity recognition, prompt evaluation, and content safety labeling. For multimodal work, computer vision annotation skills are also valuable.

  • Q: What industries and employers use data labeling teams?

    Common employers include AI labs, tech startups, BPOs, and annotation vendors supporting enterprise AI initiatives and large language model evaluation pipelines.

  • Q: How is quality measured in data labeling?

    Quality is typically measured through training data quality checks such as gold set performance, consensus review, adjudication outcomes, inter-annotator agreement, and targeted audits of high-impact categories.

230+Domains Covered
120K+PhD, Specialist, Experts Onboarded
50+Countries Represented

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