A data labeling job is a specialized role that transforms raw text, images, audio, and video into training data for AI and machine learning systems. On Rex.zone, data labelers, QA evaluators, and RLHF raters apply annotation guidelines to produce high-quality datasets used in LLM training pipelines, computer vision annotation, named entity recognition, prompt evaluation, and content safety labeling. The intent is both informational and transactional: learn what the role entails, then explore remote, contract, freelance, and full-time opportunities. By ensuring training data quality and annotation guidelines compliance, labelers directly impact model performance improvement and large language model evaluation for AI labs, tech startups, BPOs, and annotation vendors.
Data labeling professionals read, tag, categorize, and structure data so models can learn. Work spans NLP (entity tagging, sentiment), computer vision (bounding boxes, polygons, segmentation), audio (transcription, diarization), and content safety labeling (policy enforcement). Many projects include RLHF (Reinforcement Learning from Human Feedback) and prompt evaluation tasks for LLMs.
Follow annotation guidelines, label datasets, perform QA evaluation, triage edge cases, escalate ambiguities, and contribute to playbook improvements. Collaborate with ML engineers and annotation leads to ensure training data quality, maintain consistency, and optimize throughput with reviewer feedback loops and audits.
Strong attention to detail, guideline adherence, productivity discipline, and ethical judgment for content safety. Familiarity with LLM training pipelines, taxonomy design, and common tools (labeling platforms, spreadsheets). Bonus: scripting for automation, domain expertise in healthcare, legal, or e-commerce, and experience with named entity recognition or computer vision annotation.
NLP (NER, sentiment, classification), computer vision (object detection, OCR, image/video segmentation), speech and audio (ASR, speaker labeling), content moderation and safety labeling, multilingual annotation, and LLM prompt evaluation and preference ranking for RLHF.
Remote, onsite, hybrid; contract, freelance, full-time, part-time; entry-level, mid-level, senior, lead. Opportunities across AI labs, tech startups, BPOs, and annotation vendors. Rex.zone aggregates vetted roles with clear rate cards and workflow transparency.
High-quality annotations lead to model performance improvement, lower error rates, and reliable large language model evaluation. Clear taxonomies, balanced datasets, and rigorous QA maintain annotation guidelines compliance and directly influence downstream accuracy and safety.
Use platform tooling for label validation, consensus scoring, review queues, and adversarial test sets. Apply inter-annotator agreement metrics, gold standards, and sampling audits to maintain training data quality at scale.
Rex.zone connects skilled labelers with high-impact AI projects, providing transparent workflows, fair compensation, and growth paths from annotator to QA lead and RLHF rater. Navigate roles, compare employers, and manage applications in one place.
Create a Rex.zone profile, showcase domain expertise, complete a guideline comprehension quiz, and submit sample annotations. Apply to remote, contract, freelance, and full-time listings; receive feedback and project matching notifications.
Rates vary by domain complexity and seniority. Entry-level labelers start with standardized tasks; senior specialists lead QA evaluation, taxonomy design, and prompt evaluation for LLMs. Career paths extend to annotation operations, quality engineering, and model evaluation teams.
You apply detailed guidelines to tag text, images, audio, or video, resolve ambiguities, and submit labels for review. Many roles also include QA evaluation, guideline feedback, and participation in calibration sessions.
Labelers and raters rank outputs, evaluate prompts, and score responses for helpfulness, safety, and accuracy. These judgments feed RLHF pipelines that improve large language model behavior.
Attention to detail, consistency, reading comprehension, and ethical judgment. Experience with NER, computer vision annotation, content safety labeling, and familiarity with LLM training pipelines is a plus.
Yes. Entry-level roles provide training and clear guidelines. Performance and reliability can lead to senior labeling, QA lead, or RLHF rater positions.
Rex.zone lists remote, contract, freelance, and full-time roles across AI labs, tech startups, BPOs, and annotation vendors.
Through inter-annotator agreement, gold-set accuracy, sampling audits, reviewer feedback, and adherence to annotation guidelines.
Browser-based labeling platforms, review dashboards, taxonomy editors, and occasionally scripts or spreadsheets for bulk operations.
Create a profile, complete qualification tasks, and apply to roles that match your domain expertise. The platform helps track applications and onboarding.
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