[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"job-remote-data-annotator-jobs-manchester":3},{"Ques":4,"Slug":25,"Header":26,"job_category":51},{"title":5,"content":6},"Frequently Asked Questions",[7,10,13,16,19,22],{"A":8,"Q":9},"These are remote roles where you create and validate labeled training data for AI\u002FML models. Tasks commonly include data labeling, named entity recognition, computer vision annotation, content safety labeling, and LLM evaluation work such as RLHF preference ranking and prompt evaluation.","What are remote data annotator jobs in Manchester?",{"A":11,"Q":12},"Yes. The role is explicitly marked Remote and FULL_TIME, with work delivered asynchronously through online tooling and QA review workflows.","Is this role remote and full-time?",{"A":14,"Q":15},"RLHF (Reinforcement Learning from Human Feedback) is a method for improving model behavior using human preference signals. Annotators provide pairwise rankings and rationales that help train reward models and improve large language model evaluation outcomes.","What is RLHF and why is it part of data annotation?",{"A":17,"Q":18},"You will use web-based annotation tools, guideline documents, and QA evaluation workflows such as calibration tasks, gold-set checks, and reviewer feedback loops to maintain annotation guidelines compliance and training data quality.","What tools or workflows will I use?",{"A":20,"Q":21},"Highlight data annotation and data labeling experience, RLHF, prompt evaluation, QA evaluation, annotation guidelines compliance, training data quality practices, and domain exposure to NLP, named entity recognition, computer vision annotation, content safety labeling, and LLM training pipelines.","What skills should I highlight to match the job intent?",{"A":23,"Q":24},"Employers commonly include AI labs, tech startups, BPOs, and annotation vendors that build datasets and evaluation benchmarks for NLP, computer vision, and LLM training pipelines.","What types of employers hire for this work?","remote-data-annotator-jobs-manchester",{"desc":27,"title":28,"content":29},"Remote data annotator jobs in Manchester at Rex.zone focus on training-data creation for AI\u002FML systems, including data labeling, RLHF preference ranking, prompt evaluation, and QA evaluation for large language model training pipelines. You will apply annotation guidelines compliance to improve training data quality, support model performance improvement, and deliver reliable datasets for NLP, computer vision, content safety labeling, and named entity recognition workflows. This page helps you explore and apply for full-time remote data annotation work across AI labs, tech startups, BPOs, and annotation vendors through Rex.zone.","Remote Data Annotator Jobs Manchester",[30,33,36,39,42,45,48],{"h2":31,"desc":32},"Job Heading: Remote Data Annotator Jobs Manchester","Date: 25-02-2026\nCompany: Rex.zone\nCountry: US\nRemote Type: Remote\nEmployment Type: FULL_TIME\nExperience Level: Mid-Senior\nIndustry: Technology\nJob Function: Engineering\nSkills: Data annotation, data labeling, RLHF, prompt evaluation, QA evaluation, annotation guidelines compliance, training data quality, NLP, named entity recognition, computer vision annotation, content safety labeling, LLM training pipelines\nSalary Currency: USD\nSalary Min: 63360\nSalary Max: 126720\nPay Period: YEAR",{"h2":34,"desc":35},"About the Role","You will deliver high-quality labeled datasets and human feedback signals for AI\u002FML systems. The work includes RLHF tasks (pairwise preference ranking and justification), prompt evaluation, QA evaluation, and classic data labeling for NLP and computer vision. You will follow detailed annotation guidelines, document edge cases, and collaborate asynchronously with reviewers to ensure training data quality and measurable model performance improvement.",{"h2":37,"desc":38},"What You Will Do","Core responsibilities include: (1) Label and categorize text, image, and multimodal content according to taxonomy and policy, (2) Perform RLHF preference ranking to support reward modeling, (3) Execute prompt evaluation and response quality scoring for LLM evaluation, (4) Conduct QA evaluation audits, identify ambiguity, and propose guideline updates, (5) Perform named entity recognition and span labeling for NLP datasets, (6) Support content safety labeling including policy alignment and sensitive content handling, (7) Maintain annotation throughput, accuracy targets, and detailed decision logs for reviewer traceability.",{"h2":40,"desc":41},"Projects and Domains You May Support","Common project tracks include: large language model evaluation, conversational AI safety, retrieval-augmented generation grading, prompt-response pair scoring, NER dataset creation, computer vision bounding boxes and segmentation, OCR verification, content moderation and policy labeling, and multilingual NLP annotation. You may work with AI labs, tech startups, BPOs, or annotation vendors depending on client demand via Rex.zone.",{"h2":43,"desc":44},"Requirements","Required: experience with structured labeling workflows, strong written reasoning for consistent labeling decisions, ability to follow annotation guidelines compliance with high precision, and comfort working with QA review loops. Preferred: prior RLHF, prompt evaluation, LLM evaluation, NER, or computer vision annotation experience; familiarity with common error taxonomies; ability to explain disagreement cases clearly and propose guideline clarifications.",{"h2":46,"desc":47},"Quality and Performance Expectations","You will be evaluated on training data quality, inter-annotator agreement, guideline adherence, and effective handling of edge cases. Expect structured QA evaluation cycles, calibration sessions, and periodic gold-set checks. Successful annotators improve model performance by producing consistent labels, accurate preference rankings, and well-documented rationales.",{"h2":49,"desc":50},"How to Apply on Rex.zone","Apply through Rex.zone by submitting a profile that highlights relevant data labeling experience, RLHF or prompt evaluation work, and domain familiarity (NLP, computer vision annotation, content safety labeling). Include examples of guideline-driven decisions, QA experience, and any prior work improving annotation consistency or training data quality.","AI Data Operations"]