[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"job-remote-data-labeling-jobs-brussels":3},{"Ques":4,"Slug":31,"Header":32,"job_category":66},{"title":5,"content":6},"Frequently Asked Questions",[7,10,13,16,19,22,25,28],{"A":8,"Q":9},"They are remote roles for Brussels-based candidates focused on creating and validating labeled datasets used in NLP, computer vision annotation, and LLM training pipelines. Work commonly includes annotation guidelines compliance, training data quality checks, and QA evaluation.","What are remote data labeling jobs in Brussels?",{"A":11,"Q":12},"Yes. The posting is marked Remote and Employment Type is FULL_TIME, with distributed collaboration practices.","Is this role fully remote and full-time?",{"A":14,"Q":15},"Typical tasks include data labeling for text and images, named entity recognition, prompt evaluation, RLHF preference ranking, QA evaluation, and content safety labeling depending on the dataset.","What types of tasks are included?",{"A":17,"Q":18},"Emphasize data labeling and data annotation experience, RLHF and prompt evaluation, QA evaluation, named entity recognition, computer vision annotation, content safety labeling, and a track record of maintaining training data quality with strong annotation guidelines compliance.","What skills should I emphasize to match this job?",{"A":20,"Q":21},"RLHF (Reinforcement Learning from Human Feedback) uses human preference data to improve LLM behavior. In data labeling operations, you may compare model responses, choose preferred outputs, and document rationale to support model performance improvement.","What is RLHF and why does it matter here?",{"A":23,"Q":24},"AI labs, tech startups, annotation vendors, and BPO-style data operations teams use labeled data and evaluations to train and validate NLP, computer vision, and content safety systems.","What kinds of employers use this work?",{"A":26,"Q":27},"The salary range is USD 63360 to 126720 per year, as listed in the job metadata.","What compensation range is listed?",{"A":29,"Q":30},"Apply via Rex.zone by submitting your profile and highlighting relevant annotation workflows, QA evaluation experience, and examples of guideline-driven labeling that improves training data quality.","How do I apply through Rex.zone?","remote-data-labeling-jobs-brussels",{"desc":33,"title":34,"content":35},"Remote data labeling professionals in Brussels support Rex.zone AI training workflows by creating high-quality labeled datasets for NLP, computer vision, and LLM training pipelines. This full-time role focuses on annotation guidelines compliance, training data quality, RLHF and prompt evaluation, and QA evaluation to drive model performance improvement. You will label and review text, image, and multimodal data, perform content safety labeling, apply named entity recognition standards, and document edge cases so AI teams can iterate faster and deploy safer systems. Explore and apply through Rex.zone to join distributed annotation operations supporting AI labs, tech startups, and annotation vendors.","Remote Data Labeling Jobs in Brussels",[36,39,42,45,48,51,54,57,60,63],{"h2":37,"desc":38},"Remote Data Labeling Jobs in Brussels — LinkedIn Job Metadata","Title: Remote Data Labeling Specialist (Brussels)\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, data annotation, RLHF, prompt evaluation, QA evaluation, named entity recognition, computer vision annotation, content safety labeling, annotation guidelines compliance, training data quality\nSalary Currency: USD\nSalary Min: 63360\nSalary Max: 126720\nPay Period: YEAR",{"h2":40,"desc":41},"About the Role","You will deliver production-grade labeled data for machine learning teams by applying annotation guidelines, resolving ambiguity, and documenting edge cases. Work includes text labeling for NLP tasks, NER tagging, LLM prompt-response evaluation, RLHF preference judgments, and QA evaluation to ensure training data quality and model readiness.",{"h2":43,"desc":44},"What You Will Do","Execute data labeling across NLP and computer vision tasks using tool-based workflows and clear taxonomy rules. Perform RLHF and prompt evaluation by ranking responses, assessing instruction-following, and capturing rationales. Run QA evaluation on labeled batches to improve inter-annotator agreement, reduce noise, and support model performance improvement. Apply content safety labeling for policy compliance and risk mitigation. Track annotation guidelines compliance, report dataset issues, and propose rubric refinements to stakeholders.",{"h2":46,"desc":47},"Core Workstreams","NLP data annotation: classification, sentiment, intent, summarization checks, and named entity recognition. LLM evaluation: prompt evaluation, response grading, hallucination checks, groundedness and helpfulness assessments, and RLHF preference data creation. Computer vision annotation: bounding boxes, polygons, keypoints, segmentation masks, and multi-label attributes. Content safety labeling: hate\u002Fharassment, self-harm, sexual content, violence, and policy-driven risk categories. Quality assurance: sampling plans, error analysis, dispute resolution, and annotation guideline updates.",{"h2":49,"desc":50},"Required Qualifications","Mid-Senior experience in data labeling, data annotation, QA evaluation, or dataset operations. Demonstrated ability to follow annotation guidelines compliance requirements and maintain training data quality under throughput targets. Familiarity with NLP and LLM evaluation concepts such as prompt evaluation, RLHF, and rubric-based grading. Strong written reasoning for documenting edge cases, disagreements, and labeling decisions.",{"h2":52,"desc":53},"Preferred Qualifications","Experience with named entity recognition schemas and ontology\u002Ftaxonomy design. Exposure to computer vision annotation workflows (boxes, polygons, segmentation). Background in content safety labeling, trust & safety policy interpretation, or red-teaming style evaluations. Prior work with annotation vendors, BPO environments, or AI lab data operations teams.",{"h2":55,"desc":56},"Tools and Collaboration","Operate in structured labeling queues with audit trails, batch reviews, and measurement of agreement and accuracy. Collaborate asynchronously with reviewers, project leads, and ML stakeholders to clarify rubrics, refine guidelines, and close feedback loops from model performance to data improvements.",{"h2":58,"desc":59},"How Success Is Measured","High accuracy against gold standards and reviewer audits, strong inter-annotator agreement, and consistent annotation guidelines compliance. Clear documentation of edge cases and actionable error analysis that improves training data quality. Reliable throughput while maintaining precision for high-impact datasets used in large language model evaluation and model performance improvement.",{"h2":61,"desc":62},"Remote Work Notes","This is a Remote, FULL_TIME role aligned to Brussels-based candidates, with distributed collaboration across time zones. You will follow defined security and quality processes suitable for sensitive datasets used in LLM training pipelines and content safety labeling programs.",{"h2":64,"desc":65},"Apply via Rex.zone","Create or update your Rex.zone profile and apply to this Remote data labeling job aligned with Brussels. Your application should highlight relevant data annotation experience, RLHF or prompt evaluation exposure, QA evaluation practices, and examples of maintaining training data quality under detailed guidelines.","AI Data Operations"]