[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"job-senior-data-annotator-jobs-london":3},{"Ques":4,"Slug":31,"Header":32,"job_category":62},{"title":5,"content":6},"Frequently Asked Questions",[7,10,13,16,19,22,25,28],{"A":8,"Q":9},"They focus on producing high-quality training data for AI\u002FML systems via data labeling, RLHF preference data, QA evaluation, and prompt evaluation. Typical work includes annotation guidelines compliance, error analysis, and large language model evaluation to support model performance improvement.","What are senior data annotator jobs in London focused on?",{"A":11,"Q":12},"Yes. The Remote Type is Remote and the Employment Type is FULL_TIME, and the workflow is designed for remote delivery with clear QA evaluation and review processes.","Is this role remote and full-time?",{"A":14,"Q":15},"Common domains include NLP labeling (classification, summarization, extraction), named entity recognition, computer vision annotation (boxes, polygons, segmentation), content safety labeling, and RLHF and LLM evaluation workflows.","What domains are commonly covered in this role?",{"A":17,"Q":18},"RLHF (Reinforcement Learning from Human Feedback) uses human preference data and rubric-based scoring to train reward models and align LLM behavior. Senior annotators generate high-signal preference labels and ensure consistency through calibration and QA evaluation.","What is RLHF and how does it relate to data annotation?",{"A":20,"Q":21},"Skills should align with senior data annotation and evaluation workflows, including data labeling, RLHF, QA evaluation, prompt evaluation, named entity recognition, computer vision annotation, content safety labeling, LLM evaluation, annotation guidelines compliance, and training data quality.","What skills should align with this job posting intent?",{"A":23,"Q":24},"AI labs, tech startups, BPOs, and annotation vendors commonly use these workflows to build scalable LLM training pipelines and evaluation datasets.","What types of employers use these workflows?",{"A":26,"Q":27},"Quality is measured through training data quality metrics such as audit pass rates, defect rates, inter-annotator agreement, adjudication outcomes, and downstream model performance improvement signals.","How is quality measured in senior annotation work?",{"A":29,"Q":30},"Rex.zone provides the platform context for remote data annotation work, including task routing, QA evaluation workflows, guideline distribution, and review\u002Fadjudication loops used to support LLM training pipelines.","Where does Rex.zone fit into the process?","senior-data-annotator-jobs-london",{"desc":33,"title":34,"content":35},"Senior data annotator jobs in London focus on creating high-quality training data for AI\u002FML systems through data labeling, RLHF (Reinforcement Learning from Human Feedback), and QA evaluation across NLP, computer vision, and content safety workflows. At Rex.zone, you will apply annotation guidelines compliance, perform prompt evaluation and large language model evaluation, and improve training data quality to drive model performance improvement in real-world LLM training pipelines. Explore this full-time remote role to contribute to scalable annotation operations used by AI labs, tech startups, and annotation vendors.","Senior Data Annotator Jobs in London",[36,38,41,44,47,50,53,56,59],{"h2":34,"desc":37},"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, QA evaluation, prompt evaluation, named entity recognition, computer vision annotation, content safety labeling, LLM evaluation, annotation guidelines compliance, training data quality\nSalary Currency: USD\nSalary Min: 63360\nSalary Max: 126720\nPay Period: YEAR",{"h2":39,"desc":40},"About the Role","You will lead day-to-day annotation workstreams for LLM training pipelines, producing gold-standard labels and high-signal preference data for RLHF. The role spans data labeling, prompt evaluation, and QA evaluation to support model performance improvement in large language model evaluation and safety tuning. You will operate within Rex.zone workflows used by AI labs, tech startups, BPOs, and annotation vendors, ensuring training data quality and consistent annotation guidelines compliance across projects in NLP, computer vision, and content safety labeling.",{"h2":42,"desc":43},"What You Will Do","You will execute and review data annotation tasks across text, image, and multimodal datasets.\nYou will create and refine labeling taxonomies, edge-case definitions, and decision rubrics.\nYou will perform RLHF preference ranking, pairwise comparisons, and rubric-based scoring.\nYou will run QA evaluation, error analysis, and inter-annotator agreement checks.\nYou will complete prompt evaluation for instruction-following, helpfulness, and harmlessness.\nYou will label and validate named entity recognition datasets and entity linking rules.\nYou will contribute to content safety labeling for toxicity, self-harm, hate, and policy compliance.\nYou will document annotation guidelines compliance and maintain change logs for schema updates.\nYou will collaborate with engineering and data operations teams to improve tooling and throughput.\nYou will propose process improvements that increase training data quality and reduce rework.",{"h2":45,"desc":46},"Core Domains You May Work In","NLP annotation: intent, sentiment, topic, summarization, groundedness, and factuality checks.\nLLM evaluation: rubric scoring, preference data, prompt-response grading, and calibration.\nRLHF: reward modeling datasets, pairwise ranking, and policy-aligned evaluations.\nComputer vision annotation: bounding boxes, polygons, keypoints, segmentation masks, and attributes.\nContent safety labeling: policy categories, severity scoring, and sensitive content handling.\nInformation extraction: named entity recognition, relation extraction, and slot filling.",{"h2":48,"desc":49},"Requirements","Mid-senior experience in data annotation, data labeling, or AI\u002FML evaluation.\nDemonstrated ability to follow and improve annotation guidelines compliance.\nExperience with RLHF workflows, preference ranking, or rubric-based evaluation.\nStrong writing and reasoning skills for prompt evaluation and LLM evaluation.\nComfort with QA evaluation methods (sampling plans, audit trails, consistency checks).\nFamiliarity with named entity recognition and structured labeling schemas.\nExposure to computer vision annotation or content safety labeling is a plus.\nAbility to work independently in a remote environment with high attention to detail.",{"h2":51,"desc":52},"Tools and Workflow","Annotation platforms, task queues, and audit tooling used in production labeling.\nSpreadsheet-style review, disagreement resolution, and adjudication workflows.\nTaxonomy versioning, guideline updates, and annotation change management.\nBasic data handling for spot checks and QA evaluation (e.g., CSV review and filters).\nCommunication workflows for escalations, edge cases, and policy clarifications.",{"h2":54,"desc":55},"Success Metrics","Training data quality improvements measured through defect rate reduction and audit pass rates.\nHigh agreement and stable calibration across annotators and reviewers.\nFaster turnaround without sacrificing annotation guidelines compliance.\nClear documentation of edge cases, rubric updates, and QA evaluation outcomes.\nMeasurable model performance improvement signals tied to evaluation datasets.",{"h2":57,"desc":58},"Employment and Work Style","Remote: This role is explicitly Remote.\nEmployment Type: FULL_TIME.\nYou will collaborate across time zones with structured handoffs and documented decisions.\nYou will work on projects spanning contract, freelance, and full-time annotation pipelines across the Rex.zone ecosystem, while this position is full-time.",{"h2":60,"desc":61},"How to Apply","Apply through Rex.zone and include a short summary of your annotation experience (NLP, computer vision, RLHF, QA evaluation, content safety labeling). If available, share examples of guideline writing, adjudication notes, or evaluation rubrics you have used to improve training data quality.","AI Data Operations"]