[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"job-ai-data-labeling-jobs":3},{"Slug":4,"Header":5,"Ques":33,"job_category":54},"ai-data-labeling-jobs",{"desc":6,"title":7,"content":8},"AI Data Labeling Jobs at Rex.zone connect skilled annotators and evaluators with real-world AI\u002FML training workflows. This role is an entity-focused function spanning RLHF (Reinforcement Learning from Human Feedback), data labeling, QA evaluation, prompt evaluation, named entity recognition, computer vision annotation, content safety labeling, and LLM training pipelines. Candidates ensure training data quality, annotation guidelines compliance, and model performance improvement through large language model evaluation and iterative feedback loops. Apply to remote, contract, freelance, and full-time openings with AI labs, tech startups, BPOs, and annotation vendors on Rex.zone to power reliable datasets, safer products, and scalable machine learning.","AI Data Labeling Jobs",[9,12,15,18,21,24,27,30],{"h2":10,"desc":11},"Key Responsibilities","Execute high-quality annotations across NLP, computer vision, and content safety domains; follow annotation guidelines and taxonomies; conduct RLHF and prompt evaluation tasks; perform QA checks to ensure training data quality; provide structured feedback that improves model performance; participate in calibration tasks and inter-annotator agreement exercises; document edge cases and propose guideline refinements; collaborate with project managers and ML teams within LLM training pipelines.",{"h2":13,"desc":14},"Required Qualifications","Strong attention to detail and consistency; ability to interpret annotation guidelines and apply them with precision; familiarity with NLP tasks such as named entity recognition, text classification, and summarization evaluation; experience in computer vision labeling (bounding boxes, polygons, segmentation); understanding of content safety policies and trust and safety frameworks; comfort with LLM evaluation, prompt testing, and RLHF workflows; reliable internet and time management for remote assignments.",{"h2":16,"desc":17},"Preferred Experience","Hands-on work with tools like Label Studio, CVAT, SuperAnnotate, Prodigy, or similar; prior QA evaluation and guideline calibration; exposure to large language model evaluation protocols and human feedback loops; background in linguistics, cognitive science, or data operations; familiarity with inter-annotator agreement metrics and quality scoring; experience collaborating with AI labs, tech startups, BPOs, or annotation vendors.",{"h2":19,"desc":20},"Domains and Projects","NLP: NER, sentiment, intent, summarization evaluation; Computer Vision: object detection, segmentation, OCR; Content Safety: policy-based labeling for harmful, sensitive, or deceptive content; LLM Training: prompt evaluation, chat alignment, RLHF task review; Multimodal: image-text pairing, captioning, audio transcription and labeling.",{"h2":22,"desc":23},"Employment Types and Work Arrangements","Remote-first roles with flexible schedules; full-time, part-time, contract, freelance, temporary, and intern opportunities; entry-level and senior tracks; projects with AI labs, tech startups, BPOs, and annotation vendors across global time zones; performance-based progression and access to higher-complexity tasks after successful QA and calibration.",{"h2":25,"desc":26},"Impact on AI Systems","Your work drives training data quality, annotation guidelines compliance, and measurable model performance improvement. By contributing to large language model evaluation and structured feedback loops, you help build reliable AI systems that generalize better, reduce harmful outputs, and align with user expectations.",{"h2":28,"desc":29},"Tools and Workflow","Use standardized annotation platforms, project dashboards, and QA checklists; follow taxonomy documentation; complete calibration tasks; undergo periodic audits for quality assurance; collaborate via project communication channels; submit edge cases and improvement suggestions to enhance labeling efficiency and consistency.",{"h2":31,"desc":32},"How to Apply","Create your profile on Rex.zone, complete role-specific calibration tasks, pass QA, and get matched to projects that fit your skills in NLP, computer vision, content safety, and LLM training. Apply today to join curated opportunities with leading AI labs, tech startups, BPOs, and annotation vendors.",{"title":34,"content":35},"Frequently Asked Questions",[36,39,42,45,48,51],{"A":37,"Q":38},"Not always. Entry-level roles focus on guideline application and consistency; advanced roles benefit from ML literacy and LLM evaluation experience.","Is prior ML experience required?",{"A":40,"Q":41},"NLP, computer vision, content safety, RLHF, prompt evaluation, and multimodal labeling.","What domains are available?",{"A":43,"Q":44},"Full-time, part-time, contract, freelance, temporary, and internships.","What employment types are offered?",{"A":46,"Q":47},"Calibration tasks, QA audits, taxonomy documentation, and inter-annotator agreement checks.","How do projects ensure consistency?",{"A":49,"Q":50},"Yes. Strong QA scores and guideline mastery lead to senior tasks and higher rates.","Can I progress to senior roles?",{"A":52,"Q":53},"Apply on Rex.zone, complete calibration, and begin matched projects.","Where do I start?","Data Annotation & Labeling"]