Data Labeling Jobs at Rex.zone

Data labeling jobs are the core human-in-the-loop roles that create and evaluate training data for AI/ML systems on Rex.zone. As a recognized entity in AI workflows, data labeling specialists annotate text, images, audio, and video; conduct RLHF (Reinforcement Learning from Human Feedback); perform QA evaluation and prompt evaluation; and support named entity recognition, computer vision annotation, and content safety labeling. These roles directly impact training data quality, model performance improvement, and large language model evaluation inside modern LLM training pipelines. Explore remote, contract, freelance, full-time, entry-level, and senior openings across AI labs, tech startups, BPOs, and annotation vendors—all navigable on Rex.zone.

Key Responsibilities

Create high-quality annotations following detailed guidelines; label across NLP, computer vision, and multimodal datasets; perform RLHF comparisons and preference ranking; execute prompt evaluation and red-teaming; apply named entity recognition, taxonomy mapping, bounding boxes, polygons, and segmentation masks; deliver QA evaluation with issue tagging; document edge cases; collaborate on guideline updates; and contribute to calibration, pilot runs, and production sprints.

Required Qualifications

Strong attention to detail and consistency; fluent written communication; ability to follow annotation guidelines and acceptance criteria; basic understanding of machine learning data lifecycles; comfort with labeling tools (e.g., Label Studio, Prodigy, CVAT, SuperAnnotate, Scale interface); familiarity with quality metrics (precision/recall/F1); and reliable internet and workstation for remote tasks.

Preferred Experience

Hands-on RLHF or LLM evaluation; prompt engineering and safety evaluation; content policy enforcement; inter-annotator agreement processes; using sampling strategies and scoring rubrics; building taxonomies and ontologies; multilingual labeling; domain exposure in finance, healthcare, retail, or autonomous systems; and experience as QA reviewer or labeling lead.

Day-to-Day Workflows

Join projects with clear SLAs and annotation guidelines; complete calibration tasks; progress through pilot to production; follow annotation guidelines compliance checks; submit for QA review; address rework; and provide feedback loops that drive training data quality and model performance improvement. Work may include few-shot prompt crafting, rubric-based scoring, and large language model evaluation.

Domains and Project Types

NLP: classification, NER, summarization, dialogue scoring, prompt evaluation. Computer Vision: object detection, instance and semantic segmentation, keypoint and polygon annotation. Content Safety: policy labeling, harm detection, bias and fairness audits. Multimodal: image-text alignment, caption quality, grounded reasoning.

Employment Types and Locations

Opportunities include remote, contract, freelance, part-time, and full-time roles across time zones; entry-level, mid-level, and senior tracks. Employers range from AI labs and tech startups to BPOs and specialized annotation vendors. Many projects support flexible schedules and outcome-based milestones.

Quality and Tooling

Use production-grade tooling with shortcuts, templates, and consensus checks; follow double-blind reviews; leverage inter-annotator agreement, spot checks, and hierarchical QA; track issues in project dashboards; and adhere to data security, privacy, and confidentiality standards.

Career Growth

Advance from annotator to QA reviewer, team lead, guideline designer, project manager, or data operations specialist. Build cross-domain expertise in NLP, computer vision, and content safety; expand into model evaluation, red-teaming, and data governance.

How to Apply on Rex.zone

Create your Rex.zone profile, list domain strengths, and complete skill verifications. Opt into benchmark tasks for RLHF, NER, CV annotation, and safety labeling. Set location and availability preferences, connect payment details, and start applying to projects that match your skills.

Frequently Asked Questions

Q: Who hires data labelers on Rex.zone?
AI labs, tech startups, BPOs, and annotation vendors post roles ranging from short-term projects to full-time positions.
Q: Do I need ML experience?
Not always. Understanding guidelines and quality standards is essential; RLHF and model evaluation experience helps for senior roles.
Q: What are typical tasks?
NER, text classification, prompt and response scoring, safety policy labeling, object detection, segmentation, and QA review.
Q: Can I work freelance?
Yes. Many projects are contract or freelance with flexible schedules and remote-first workflows.
Q: How is confidentiality handled?
Projects require adherence to data privacy, NDAs, and secure tool usage. Access is scoped per role and project.
Q: What improves my chances?
Complete benchmark tasks, maintain high QA scores, document edge cases, and demonstrate guideline compliance and consistency.

230+Domains Covered

120K+PhD, Specialist, Experts Onboarded

50+Countries Represented

Industry-Leading Compensation

We believe exceptional intelligence deserves exceptional pay. Our platform consistently offers rates above the industry average, rewarding experts for their true value and real impact on frontier AI. Here, your expertise isn't just appreciated—it's properly compensated.

Work Remotely, Work Freely

No office. No commute. No constraints. Our fully remote workflow gives experts complete flexibility to work at their own pace, from any country, any time zone. You focus on meaningful tasks—we handle the rest.

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