Remote Data Labeling Jobs in Brussels

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.

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Remote Data Labeling Jobs in Brussels — LinkedIn Job Metadata

Title: Remote Data Labeling Specialist (Brussels) Date: 25-02-2026 Company: Rex.zone Country: US Remote Type: Remote Employment Type: FULL_TIME Experience Level: Mid-Senior Industry: Technology Job Function: Engineering Skills: Data labeling, data annotation, RLHF, prompt evaluation, QA evaluation, named entity recognition, computer vision annotation, content safety labeling, annotation guidelines compliance, training data quality Salary Currency: USD Salary Min: 63360 Salary Max: 126720 Pay Period: YEAR

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.

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.

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/harassment, self-harm, sexual content, violence, and policy-driven risk categories. Quality assurance: sampling plans, error analysis, dispute resolution, and annotation guideline updates.

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.

Preferred Qualifications

Experience with named entity recognition schemas and ontology/taxonomy 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.

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.

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.

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.

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.

Frequently Asked Questions

  • Q: What are remote data labeling jobs in Brussels?

    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.

  • Q: Is this role fully remote and full-time?

    Yes. The posting is marked Remote and Employment Type is FULL_TIME, with distributed collaboration practices.

  • Q: What types of tasks are included?

    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.

  • Q: What skills should I emphasize to match this job?

    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.

  • Q: What is RLHF and why does it matter here?

    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.

  • Q: What kinds of employers use this work?

    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.

  • Q: What compensation range is listed?

    The salary range is USD 63360 to 126720 per year, as listed in the job metadata.

  • Q: How do I apply through Rex.zone?

    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.

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