Data Labeling Jobs — RLHF, LLM Evaluation, Computer Vision Annotation at Rex.zone

Data Labeling Jobs at Rex.zone connect skilled annotators with AI labs, tech startups, BPOs, and annotation vendors building production ML systems. As a search-recognizable entity, a data labeling specialist performs RLHF preference ranking, QA evaluation, prompt evaluation, named entity recognition, computer vision annotation, and content safety labeling to fuel LLM training pipelines. Apply to improve training data quality, ensure annotation guidelines compliance, and drive model performance improvement through large language model evaluation and error analysis. Roles span NLP, vision, and multimodal tasks; openings include remote, contract, freelance, full-time, entry-level, and senior positions. Explore opportunities on Rex.zone and join high-impact workflows powering state-of-the-art AI.

About the Role

Data labeling specialists create structured, high-quality annotations that train, evaluate, and safeguard machine learning systems. You will work across NLP, computer vision, and multimodal datasets, applying taxonomies and guidelines to produce consistent labels that directly influence model performance, safety, and reliability. Projects span RLHF preference ranking, prompt evaluation, named entity recognition, bounding boxes and segmentation, and policy-aligned content safety labeling.

Key Responsibilities

Follow detailed annotation guidelines; execute RLHF preference ranking for dialogue and instruction tuning; conduct prompt evaluation and chain-of-thought reviews; perform named entity recognition, intent classification, and sentiment; deliver computer vision annotation including bounding boxes, polygons, and keypoints; apply content safety policies to rate risk and compliance; complete QA evaluation passes, debug edge cases, and document rationales; contribute to schema design, taxonomy updates, and golden set curation.

Required Skills & Tools

Strong attention to detail, guideline comprehension, and consistency; familiarity with LLM evaluation concepts, error analysis, and model feedback loops; capability in CV annotation standards and formats; experience with tools like Label Studio, CVAT, Prodigy, or internal platforms; comfort with quality metrics such as inter-annotator agreement and spot checks; privacy awareness and secure handling of sensitive content; clear written communication, with multilingual capabilities a plus.

Workflows in LLM Training Pipelines

Typical pipelines include data sourcing, de-duplication, first-pass labeling, QA evaluation, and large language model evaluation. RLHF workflows add preference ranking, rubric alignment, and calibration rounds. Teams maintain golden sets, run periodic audits, measure training data quality, and track annotation guidelines compliance. Findings feed back into prompt engineering, taxonomies, and model performance improvement through iterative retraining.

Employment Types & Locations

Opportunities include remote, contract, freelance, part-time, and full-time roles, with both entry-level and senior paths. Projects are offered by AI labs, tech startups, BPOs, and annotation vendors across NLP, computer vision, content safety, and LLM training. Most work is remote with timezone-flexible schedules; limited on-site roles may be available for secured environments.

Quality Assurance & Metrics

Quality is measured through inter-annotator agreement, guideline adherence, latency, and accuracy against golden sets. QA evaluation includes sampling, double-pass review, and discrepancy resolution. Continuous monitoring drives training data quality and model performance improvement, ensuring annotation guidelines compliance and robust large language model evaluation for production readiness.

Why Rex.zone

Rex.zone centralizes vetted data labeling jobs and standardized workflows. Gain access to calibrated tasks, transparent guidelines, and reliable payments. Collaborate with reputable employers, track performance metrics, and build a portfolio across high-impact projects. The platform supports clear navigation—from discovery and application to onboarding and delivery—so you can find the right role and grow your career.

How to Apply

Create your profile on Rex.zone, list domain strengths and tools, and upload sample annotations or a portfolio. Complete calibration tasks, pass guideline quizzes, and accept relevant NDAs. Once approved, you can claim jobs by domain, availability, and pay structure. Apply now to start labeling across RLHF, prompt evaluation, NER, computer vision, and content safety.

Career Growth

Advance from annotator to QA reviewer, domain lead, taxonomy designer, or RLHF operator. Senior contributors may specialize in guideline authorship, training program development, and evaluation engineering. Demonstrated excellence in accuracy, throughput, and documentation can open paths into annotation management and ML data operations.

Frequently Asked Questions

Q: What is a data labeling job at Rex.zone?
It is a specialized annotation role supporting AI/ML workflows. You label text, images, audio, and multimodal data for RLHF, prompt evaluation, named entity recognition, computer vision annotation, and content safety labeling, contributing directly to LLM training pipelines and model evaluation.
Q: Which domains are available?
NLP (NER, intent, sentiment, summarization), computer vision (bounding boxes, polygons, keypoints), content safety (policy-based ratings), and LLM training tasks (RLHF preference ranking, large language model evaluation, prompt evaluation). Multimodal projects are also available.
Q: Are roles remote or on-site?
Most roles are remote, with contract, freelance, and full-time options. Some employers may offer on-site positions for secure environments or specialized hardware workflows.
Q: Do you hire entry-level and senior annotators?
Yes. Entry-level candidates complete calibration and training. Senior annotators lead complex tasks, mentor teams, and own guideline interpretation. Both paths emphasize accuracy and annotation guidelines compliance.
Q: How is compensation structured?
Compensation varies by domain, complexity, and employer type (AI labs, tech startups, BPOs, annotation vendors). Payment may be per-task, per-hour, or per-project, with bonuses for quality, throughput, and reliability.
Q: What tools will I use?
Common tools include Label Studio, CVAT, Prodigy, and employer-specific platforms. You will follow standardized workflows on Rex.zone with clear instructions, golden sets, and QA evaluation checkpoints.
Q: How is quality measured?
Quality uses inter-annotator agreement, accuracy to golden sets, guideline quiz performance, and audit outcomes. These metrics ensure training data quality and support model performance improvement.
Q: How do I apply on Rex.zone?
Create a profile, upload samples, complete calibration tasks, pass guideline checks, and accept NDAs. Then claim suitable jobs filtered by remote, contract, freelance, full-time, entry-level, or senior settings.
Q: Is prior experience required?
Not always. Entry-level roles include training and calibration. Demonstrating attention to detail, consistency, and good documentation is key. Senior roles require proven experience across domains.
Q: How do you handle sensitive data and privacy?
Employers enforce NDAs, secure environments, and data minimization. Annotators follow privacy rules, avoid PII exposure, and comply with content safety labeling policies.
Q: What is RLHF and how will I participate?
RLHF is Reinforcement Learning from Human Feedback. You compare model outputs, rank preferences, apply rubrics, and provide rationales that guide alignment and large language model evaluation.
Q: Who are the typical employers?
AI labs, tech startups, BPOs, and specialized annotation vendors. Projects range from research prototypes to production-scale data pipelines.

230+Domains Covered

120K+PhD, Specialist, Experts Onboarded

50+Countries Represented

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