This page recruits for the data labeling job entity: entry-level and experienced data annotation specialists supporting AI/ML training pipelines at Rex.zone. Workflows include RLHF (Reinforcement Learning from Human Feedback), prompt evaluation for large language models, QA evaluation, named entity recognition, computer vision annotation, and content safety labeling. Intent is transactional (apply now), informational (understand tasks, tools, and quality standards), and navigational (join projects on Rex.zone). Data labelers improve training data quality, ensure annotation guidelines compliance, and contribute to model performance improvement and large language model evaluation. Remote, flexible roles are available across NLP, computer vision, and content moderation for AI labs, tech startups, BPOs, and annotation vendors.
As a Data Labeling Specialist, you will annotate text, images, audio, and video to produce high-quality datasets used in machine learning and LLM training. Your labels guide model behavior, safety, and accuracy across tasks like classification, tagging, transcription, extraction, and preference ranking for RLHF.
Apply detailed annotation guidelines; review prompts and responses for LLMs; conduct QA evaluation; perform named entity recognition; tag computer vision examples; label content safety categories; and provide structured feedback that drives training data quality and model performance improvement.
NLP: sentiment, NER, intent classification, summarization. Computer vision: bounding boxes, polygons, image classification. Content safety: toxicity, self-harm, hate, adult content. LLM training: RLHF preference ranking, prompt evaluation, response scoring, tool-use verification.
No experience required for entry-level. Rex.zone provides onboarding, sample tasks, guideline walkthroughs, calibration tests, and shadow assignments. Senior contributors may enter advanced queues for RLHF and complex QA workflows after passing qualification rounds.
Attention to detail; reliable internet; fluent reading comprehension; ability to follow annotation guidelines; basic computer literacy; consistency in labeling; willingness to learn domain-specific taxonomies.
Prior annotation experience; familiarity with LLM training pipelines; knowledge of NER, CV labeling tools, and content safety taxonomies; QA evaluation expertise; excellent documentation and feedback skills.
Use Rex.zone workspaces, browser-based labeling tools, and project-specific dashboards. Some projects integrate third-party platforms for image annotation, transcription, and RLHF preference ranking with secure single sign-on.
You will complete tasks, pass guideline checks, and undergo review by QA leads. Metrics include accuracy, coverage, consistency, and annotation guidelines compliance. Feedback loops help you improve over time and qualify for higher-paying queues.
Remote roles with flexible schedules. Opportunities include contract, freelance, part-time, and full-time. Entry-level and senior tracks available. Some queues are on-demand; others follow fixed weekly allocations.
Pay varies by project type and geography. Typical entry-level ranges are USD $12–$25 per hour, or per-task rates for microtasks. Senior RLHF and QA evaluation queues may pay more. Some projects offer bonuses for quality, volume, and reliability.
AI labs, tech startups, BPOs, and specialized annotation vendors source talent via Rex.zone. Projects span NLP, computer vision, content safety, and large language model evaluation at various stages of the AI lifecycle.
Start entry-level, build a portfolio, then progress to senior annotator, QA reviewer, guideline author, or project lead. Experience in RLHF, training data quality, and LLM evaluation strengthens your ML operations career path.
Work from home with a stable internet connection and a laptop or desktop. Some projects require quiet environments, webcam verification, or NDAs to protect data integrity and privacy.
Create your profile on Rex.zone, complete the skills survey, and take calibration tests. Once qualified, you can join available queues that match your interests and schedule. Apply today to start labeling.
Data labeling is the process of annotating text, images, audio, and video with structured tags so machine learning models can learn patterns and generate accurate outputs.
No. Entry-level roles require no experience. You will receive training, guidelines, and calibration tasks on Rex.zone before joining production queues.
A computer, reliable internet, and a modern browser. Certain projects may require headphones for audio tasks or a webcam for identity verification.
After onboarding and passing calibrations, you can pick available queues. Some are first-come-first-served; others are scheduled or require qualification tests.
Compensation may be hourly or per-task. Entry-level rates often range from USD $12–$25 per hour, with higher rates for RLHF, QA evaluation, and senior roles.
Calibration tasks may be unpaid or lightly paid depending on the project. Production work is compensated according to the queue’s rate card.
Quality is tracked via accuracy, consistency, coverage, and guideline adherence. You may receive feedback and must maintain thresholds to remain in a queue.
Remote and flexible. Options include freelance, contract, part-time, and full-time. Availability and quotas vary by project and timezone.
Yes. Strong performance can lead to senior annotator, QA reviewer, guideline author, and project lead positions with higher pay and responsibility.
Many projects are open globally, but content and compliance requirements can vary by region. Some queues prioritize specific language fluency.
Content safety labeling may involve exposure to sensitive material. Rex.zone provides clear guidelines and opt-in policies for such queues.
Create an account on Rex.zone, complete your profile, take the onboarding tests, and select the queues that match your skills and availability.
Rex.zone is a platform that connects talent with AI labs, tech startups, BPOs, and annotation vendors for data labeling, RLHF, QA evaluation, and LLM training projects.
The terms are often used interchangeably. Data annotation typically refers to structured tagging, while labeling emphasizes the final tags used to train models.
Response times vary by project demand. Most candidates receive status updates within 3–7 business days after completing calibration steps.
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