Data Labeling Jobs Online

Data Labeling Jobs Online at Rex.zone connects skilled annotators with real AI/ML training workflows. As a data annotation specialist, you create and evaluate training data across NLP, computer vision, and content safety, powering LLM training pipelines and model performance improvement. Typical tasks include RLHF preference judgments, prompt evaluation, QA evaluation, named entity recognition, bounding-box and segmentation work, and large language model evaluation, all executed with strict annotation guidelines compliance to ensure training data quality. Explore remote, contract, freelance, and full-time roles with AI labs, tech startups, BPOs, and annotation vendors on Rex.zone—apply once, get matched continuously.

Key Responsibilities

Produce high-quality annotations for NLP and computer vision datasets; perform RLHF preference comparisons and prompt evaluation for large language models; run QA evaluation on completed tasks to maintain training data quality; follow annotation guidelines compliance and taxonomy standards; document edge cases and escalate ambiguities; contribute to model performance improvement by spotting systemic issues; support large language model evaluation (instruction following, safety, and factuality); execute content safety labeling across policy tiers; participate in calibration sessions and inter-annotator agreement reviews.

Required Qualifications

Strong attention to detail and consistency; proven ability to follow complex instructions and annotation schemas; experience with named entity recognition (NER), text classification, sentiment, and span labeling; familiarity with computer vision annotation (bounding boxes, polygons, keypoints); comfort evaluating LLM outputs, prompts, and conversational chains; understanding of QA workflows, sampling, and rubric-based scoring; reliable internet and remote-ready workspace; English proficiency. Bonus: multilingual skills, prior RLHF or content safety work, basic Python or SQL for data handling.

Domains and Workflows

NLP: NER, topic classification, summarization, prompt evaluation; Computer Vision: object detection, instance segmentation, keypoint labeling; Content Safety: policy-based labeling for toxicity, privacy, hate, self-harm; LLM Training: RLHF preference ranking, chain-of-thought checks, large language model evaluation; Data Quality: gold set creation, rubric design, guideline iteration, inter-annotator agreement measurement.

Role Types and Schedules

Openings include remote, contract, freelance, part-time, and full-time placements. Opportunities span entry-level to senior lead roles. Employers on Rex.zone range from AI labs and tech startups to BPOs and specialized annotation vendors. Flexible hours are common, with some projects requiring timezone overlap or weekend availability during high-priority model training cycles.

Tools and Platforms

Work with modern labeling and evaluation tools such as Label Studio, Prodigy, LightTag, and CVAT; collaborate via Jira/Asana; manage data securely through cloud storage; and follow documented SOPs and annotation guidelines. Familiarity with spreadsheet QA templates and rubric scoring portals is a plus.

Compensation and Growth

Competitive hourly or per-task rates based on task complexity, language specialization, and quality metrics. Senior contributors can progress to reviewer, auditor, or project lead, mentoring teams and refining guidelines to drive model performance improvement and training data quality.

How to Apply on Rex.zone

Create your free Rex.zone profile, select domains (NLP, computer vision, content safety, RLHF), complete quick skills checks, and sign the standard NDA. You may be invited to paid pilot tasks. After calibration, you’ll receive project matches aligned to your level—entry-level, mid, or senior—across remote, contract, freelance, and full-time roles.

Data Labeling Jobs Online — FAQs

Q: What does a data labeler do on RLHF and LLM evaluation projects?
You compare model responses via pairwise ranking, score prompts and outputs against rubrics, and assess safety and factuality. These judgments help optimize reward models and improve large language model performance.
Q: Are these roles remote and flexible?
Yes. Most Rex.zone listings are fully remote with flexible schedules. Options include freelance, contract, part-time, and full-time placements across multiple time zones.
Q: Do I need prior experience?
Entry-level roles exist, especially for straightforward classification or bounding-box tasks. Senior and reviewer tracks require prior annotation, QA evaluation, or guideline design experience.
Q: How is quality measured and paid?
Quality is evaluated using rubric alignment, inter-annotator agreement, and spot checks with gold data. Compensation may be hourly or per-task, with bonuses for consistent, high-quality work.
Q: Which domains are in demand?
NLP (NER, summarization), computer vision (detection, segmentation), content safety labeling, and RLHF/prompt evaluation for LLM training pipelines are in high demand.
Q: How do I apply on Rex.zone?
Create a profile, complete skills checks, pass a short calibration, and you’ll be matched to suitable remote projects from AI labs, tech startups, BPOs, and annotation vendors.

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