[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"job-remote-data-annotator-jobs-barcelona":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},"Yes. The Remote Type for this position is Remote, and the workflows are designed for distributed delivery with defined QA evaluation and security procedures.","Are these roles fully remote?",{"A":11,"Q":12},"A Remote Data Annotator creates labeled examples and evaluations used in LLM training pipelines and model assessment, including data labeling, RLHF preference ranking, prompt evaluation, named entity recognition, computer vision annotation, and content safety labeling.","What does a Remote Data Annotator do in AI\u002FML training?",{"A":14,"Q":15},"Yes. The Employment Type is FULL_TIME.","Is this a full-time role?",{"A":17,"Q":18},"The Experience Level is Mid-Senior, meaning you should be comfortable working independently, following complex rubrics, and contributing to training data quality and QA evaluation processes.","What experience level is expected?",{"A":20,"Q":21},"Emphasize data annotation, data labeling, RLHF, QA evaluation, prompt evaluation, named entity recognition, computer vision annotation, and content safety labeling, along with examples of annotation guidelines compliance and quality outcomes.","What skills should I emphasize when applying?",{"A":23,"Q":24},"Projects may support AI labs, tech startups, BPOs, and annotation vendors that need high-quality training data and large language model evaluation at scale.","What kinds of companies use this work?",{"A":26,"Q":27},"Quality is ensured through annotation guidelines compliance checks, gold-standard calibration, peer review, adjudication, and QA evaluation metrics focused on accuracy, consistency, and error pattern reduction.","How is quality ensured?",{"A":29,"Q":30},"The salary range is USD 63360 to USD 126720 per YEAR, depending on project scope, quality performance, and relevant domain experience.","What is the salary range for this job posting?","remote-data-annotator-jobs-barcelona",{"desc":33,"title":34,"content":35},"Remote Data Annotator Jobs Barcelona at Rex.zone focus on data labeling and human-in-the-loop evaluation that improve AI\u002FML model performance in real production training pipelines. You will annotate text, images, audio, and video; follow annotation guidelines compliance; and support RLHF, QA evaluation, prompt evaluation, and content safety labeling for large language model evaluation and computer vision annotation. This full-time remote role connects your labeling work directly to training data quality, model performance improvement, and scalable LLM training pipelines used by AI labs, tech startups, and annotation vendors.","Remote Data Annotator Jobs Barcelona",[36,38,41,44,47,50,53,56,59],{"h2":34,"desc":37},"Title: Remote Data Annotator Jobs 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 annotation, data labeling, RLHF, QA evaluation, prompt evaluation, named entity recognition, computer vision annotation, content safety labeling\nSalary Currency: USD\nSalary Min: 63360\nSalary Max: 126720\nPay Period: YEAR",{"h2":39,"desc":40},"About the Role","As a Remote Data Annotator, you will produce and validate labeled datasets used to train and evaluate AI systems across NLP, computer vision, and multimodal workflows. You will apply detailed rubrics, resolve edge cases, and collaborate with project leads to improve training data quality and consistency. Your output supports large language model evaluation, RLHF preference data, prompt evaluation, and content safety labeling, helping teams ship safer and more accurate models.",{"h2":42,"desc":43},"What You Will Do","You will:\n- Perform data labeling for text, image, audio, and video tasks using project-specific tools.\n- Create RLHF comparisons and rankings to support reinforcement learning from human feedback.\n- Execute QA evaluation checks (accuracy, consistency, ambiguity handling) and report systematic issues.\n- Conduct prompt evaluation and response grading for large language model evaluation.\n- Apply named entity recognition tagging, classification, and extraction based on annotation guidelines.\n- Complete computer vision annotation (bounding boxes, polygons, keypoints, segmentation) when needed.\n- Perform content safety labeling for policy categories such as hate, harassment, sexual content, and self-harm.\n- Maintain clear audit trails, follow data handling procedures, and meet throughput targets without sacrificing quality.",{"h2":45,"desc":46},"Workflows and Task Types","Common workflows include:\n- Training data quality reviews and annotation guidelines compliance audits.\n- LLM training pipelines support via preference ranking, factuality checks, and instruction-following scoring.\n- NLP tasks such as sentiment labeling, topic classification, summarization evaluation, and NER.\n- Computer vision annotation for object detection, image classification, OCR verification, and segmentation.\n- Safety evaluation and policy enforcement labeling to improve model robustness.\n- Multi-pass review processes (initial label → peer review → adjudication → gold set calibration).",{"h2":48,"desc":49},"Required Qualifications","You have:\n- Experience in data annotation or data labeling with measurable quality outcomes.\n- Strong ability to follow detailed rubrics and resolve ambiguous cases consistently.\n- Familiarity with QA evaluation concepts such as inter-annotator agreement and error categorization.\n- Comfort working with NLP and\u002For computer vision datasets and tooling.\n- Clear written communication for issue reporting, adjudication notes, and guideline feedback.\n- Ability to work full-time in a remote environment with reliable connectivity and secure work practices.",{"h2":51,"desc":52},"Preferred Qualifications","You may also have:\n- Hands-on exposure to RLHF, preference modeling datasets, or prompt evaluation programs.\n- Experience with named entity recognition, taxonomy design, or ontology-based labeling.\n- Background in content safety labeling, trust and safety operations, or policy QA.\n- Experience supporting AI labs, tech startups, BPOs, or annotation vendors on multi-client programs.\n- Familiarity with evaluation metrics and systematic approaches to model performance improvement.",{"h2":54,"desc":55},"How Success Is Measured","Success is measured by:\n- High annotation accuracy and consistency against gold standards.\n- Strong annotation guidelines compliance and low rework rates.\n- Effective QA evaluation participation and actionable defect reporting.\n- Reliable throughput while maintaining training data quality.\n- Contribution to model performance improvement through pattern detection and guideline refinements.",{"h2":57,"desc":58},"Why Rex.zone","Rex.zone connects qualified remote professionals with data annotation and AI\u002FML evaluation projects across domains such as NLP, computer vision, content safety, and LLM training pipelines. You will work in structured programs that emphasize training data quality, clear rubrics, and QA evaluation practices, with opportunities to expand into RLHF and prompt evaluation tracks.",{"h2":60,"desc":61},"Apply","To apply for Remote Data Annotator Jobs Barcelona, submit your application through Rex.zone. Highlight relevant data labeling experience, QA evaluation practices, RLHF or prompt evaluation exposure, and any NLP, named entity recognition, or computer vision annotation background.","AI Data Operations"]