[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"job-senior-data-labeling-jobs-barcelona":3},{"Ques":4,"Slug":25,"Header":26,"job_category":54},{"title":5,"content":6},"Frequently Asked Questions",[7,10,13,16,19,22],{"A":8,"Q":9},"Yes. The role is explicitly marked Remote. The Barcelona keyword aligns with search intent for senior data labeling jobs tied to Barcelona, while the work arrangement remains Remote.","Is this a remote role even though the keyword includes Barcelona?",{"A":11,"Q":12},"You will lead data labeling and evaluation across RLHF preference data, prompt evaluation, named entity recognition, computer vision annotation, and content safety labeling, with an emphasis on training data quality and annotation guidelines compliance.","What types of data labeling tasks will I lead?",{"A":14,"Q":15},"Quality is measured through QA evaluation methods such as sampling audits, adjudication, inter-annotator agreement, guideline violation rates, and error taxonomy tracking tied to model performance improvement.","How is quality measured in this role?",{"A":17,"Q":18},"Standard labeling often focuses on ground truth classification or structured tags, while RLHF work frequently involves preference ranking, rubric-based judgments, and prompt evaluation designed to shape model behavior in LLM training pipelines.","What is the difference between RLHF tasks and standard labeling?",{"A":20,"Q":21},"The workflows are commonly used by AI labs, tech startups, BPOs, and annotation vendors building datasets for NLP, computer vision, content safety, and large language model evaluation.","Which employer types can this work support?",{"A":23,"Q":24},"Highlight training data quality ownership, annotation guidelines compliance, QA evaluation, adjudication and calibration, edge-case reasoning, RLHF and prompt evaluation familiarity, named entity recognition knowledge, and experience coordinating multi-reviewer workflows.","What skills should I highlight for a senior data labeling role?","senior-data-labeling-jobs-barcelona",{"desc":27,"title":28,"content":29},"Senior data labeling jobs in Barcelona at Rex.zone focus on training data quality for AI systems across NLP, computer vision, and LLM training pipelines. You will lead annotation workflows, refine annotation guidelines compliance, and run QA evaluation to drive model performance improvement. This remote, full-time role supports RLHF, prompt evaluation, named entity recognition, content safety labeling, and multimodal dataset curation used by AI labs, tech startups, and annotation vendors. If you build reliable ground truth, manage edge cases, and improve reviewer consistency, explore and apply through Rex.zone to help ship safer, higher-accuracy models.","Senior Data Labeling Jobs in Barcelona",[30,33,36,39,42,45,48,51],{"h2":31,"desc":32},"Job Overview","Title: Senior Data Labeling Specialist Barcelona\nDate: 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 labeling, training data quality, annotation guidelines compliance, QA evaluation, RLHF, prompt evaluation, 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 lead senior-level data labeling and evaluation workflows for ML datasets used in large language model evaluation and multimodal model training. This includes building and enforcing annotation guidelines, resolving ambiguous edge cases, calibrating annotators, and improving inter-annotator agreement. You will partner with engineering and research stakeholders to translate model goals into labeling taxonomies, run sampling-based QA evaluation, and deliver training data quality improvements that measurably impact offline metrics and online model behavior.",{"h2":37,"desc":38},"Key Responsibilities","Own annotation project setup: taxonomy design, label definitions, and gold-standard examples for consistent ground truth creation\nLead RLHF support tasks, including preference ranking and prompt evaluation aligned to safety, helpfulness, and factuality rubrics\nDrive QA evaluation programs: audits, double-pass review, error categorization, and root-cause analysis for systematic issues\nSupport NLP labeling tasks such as named entity recognition, entity linking, classification, and retrieval relevance judgments\nSupport computer vision annotation workflows such as bounding boxes, polygons, segmentation masks, and attribute tagging when needed\nOperationalize content safety labeling guidelines for policy, toxicity, self-harm, and sensitive content categories\nReport training data quality KPIs, reviewer consistency metrics, and model performance improvement insights to stakeholders\nCoordinate with annotation vendors or BPO teams when applicable; ensure throughput and accuracy targets are met\nContinuously iterate guidelines and tooling feedback to reduce ambiguity and improve labeling efficiency",{"h2":40,"desc":41},"Required Qualifications","Mid-Senior experience in data labeling, data annotation, or ML data operations supporting production AI\u002FML systems\nStrong understanding of annotation guidelines compliance, rubric design, and quality assurance methods\nHands-on experience with QA evaluation workflows such as audits, adjudication, and calibration sessions\nFamiliarity with LLM training pipelines, including RLHF concepts, preference data, and prompt evaluation best practices\nAbility to reason about ambiguity, write clear labeling instructions, and handle long-tail edge cases consistently\nComfort collaborating cross-functionally with engineering, applied ML, product, and operations teams",{"h2":43,"desc":44},"Preferred Qualifications","Experience with named entity recognition and other structured NLP labeling tasks\nExperience with computer vision annotation, including segmentation and attribute tagging\nExperience labeling content safety datasets and interpreting policy-driven rubrics\nExperience with evaluation design for large language model evaluation and reviewer agreement measurement\nExposure to annotation tooling, workflow automation, or dataset versioning practices",{"h2":46,"desc":47},"What Success Looks Like","Training data quality improves through fewer guideline violations, lower rework rates, and higher reviewer agreement\nAnnotation guidelines are clear, testable, and resilient to edge cases across domains like NLP and computer vision\nQA evaluation identifies systematic failure modes and closes the loop with actionable guideline updates\nRLHF and prompt evaluation outputs are consistent, well-calibrated, and useful for model performance improvement\nStakeholders can trace dataset changes to measurable improvements in model evaluation and production behavior",{"h2":49,"desc":50},"Work Modality and Role Fit","Remote: Yes, this role remains Remote\nFull-time: Yes, FULL_TIME employment type\nAlso relevant to searches for: remote, full-time, contract, freelance, entry-level, and senior data labeling roles across AI labs, tech startups, BPOs, and annotation vendors\nDomains supported: NLP, computer vision, content safety, and LLM training pipelines",{"h2":52,"desc":53},"How to Apply on Rex.zone","Prepare a short summary of your data labeling and QA evaluation experience, including guideline design and audit methods\nList domains you have supported: RLHF, prompt evaluation, named entity recognition, computer vision annotation, or content safety labeling\nApply through Rex.zone and be ready to discuss training data quality, edge-case handling, and reviewer calibration approaches","AI Data Operations"]