Remote Data Labeling Specialist

Rex.zone is seeking a Remote Data Labeling Specialist in Brussels to support AI training workflows by creating and validating high-quality labeled datasets for NLP, computer vision, and LLM training pipelines. This full-time role requires annotation guideline compliance, training data quality focus, RLHF and prompt evaluation, and QA evaluation. Apply now at remoexperts.com.

Looking for a Remote Data Labeling Specialist at Rex.zone in Brussels, Brussels Region, Belgium? This role focuses on the day-to-day work of a Remote Data Labeling Specialist.

About The Role Remote data labeling professionals in Brussels support Rex.zone AI training workflows by creating and validating high-quality labeled datasets for NLP, computer vision, and LLM training pipelines. This full-time role focuses on annotation guidelines compliance, tra

Company Overview

RemoExperts is a global platform where skilled professionals and tutors contribute to AI training across video, image, audio, text, code, math, and more. We are building a community of students, experts, and freelancers who work remotely, earn fairly, and play a direct role in teaching AI to be safer and perform better. No prior AI experience is required.

Quick Details

• Salary Range: $30.00/hr - $50.00/hr
• Job Type: Full-time
• Experience: Mid-Senior level

Key Responsibilities

• Execute data labeling for text, images, and multimodal datasets using tool-based workflows and taxonomy rules
• Perform named entity recognition (NER) tagging and other NLP annotation tasks
• Conduct LLM prompt evaluation and RLHF preference ranking; document clear rationales
• Run QA evaluation on labeled batches to improve inter-annotator agreement and reduce noise
• Apply content safety labeling using policy-driven risk categories and escalation rules
• Track annotation guidelines compliance, resolve ambiguity, and document edge cases

Requirements

• Mid-Senior experience in data labeling, data annotation, QA evaluation, or dataset operations
• Proven ability to maintain training data quality under throughput targets
• Familiarity with NLP/LLM evaluation concepts (prompt evaluation, RLHF, rubric-based grading)
• Strong written reasoning for documenting disagreements and labeling decisions

Benefits

• Work in structured labeling queues with audit trails, batch reviews, and measurement of agreement and accuracy.
• Collaborate asynchronously across time zones with reviewers, project leads, and ML stakeholders.
• Contribute to teaching AI to be safer and perform better.
• Earn fairly while working remotely.