[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"job-jobs-at-data-annotation":3},{"Slug":4,"Header":5,"Ques":27,"job_category":42},"jobs-at-data-annotation",{"desc":6,"title":7,"content":8},"Data Annotation Jobs are specialized roles within AI\u002FML training pipelines. At Rex.zone, data annotation specialists, data labeling experts, RLHF evaluators, and QA reviewers create high-quality training data for LLMs, NLP, computer vision, and content safety systems. Work includes prompt evaluation, named entity recognition, image\u002Fvideo annotation, and guideline-compliant labeling that drives model performance improvement. Explore remote, contract, freelance, and full-time opportunities with AI labs, tech startups, BPOs, and annotation vendors on Rex.zone.","Data Annotation Jobs",[9,12,15,18,21,24],{"h2":10,"desc":11},"Key Responsibilities","Perform text, image, audio, and video labeling; follow annotation guidelines and taxonomies; execute RLHF pairwise\u002Franking evaluations; conduct prompt evaluation and response scoring; handle named entity recognition (NER), sentiment, intent, and topic tagging; annotate bounding boxes, segmentation, OCR, and keypoints for computer vision; apply content safety labeling policies; run multi-tier QA checks, consensus scoring, and inter-annotator agreement measurement; document edge cases and escalate ambiguous samples; collaborate with model engineers and QA leads to improve training data quality.",{"h2":13,"desc":14},"Required Qualifications","1+ year in data annotation or related QA; strong attention to detail and guideline compliance; familiarity with LLM evaluation, RLHF, and prompt scoring; experience with NER, NLP taxonomies, or computer vision labeling; ability to use annotation tools and productivity trackers; English proficiency; reliable internet for remote work; understanding of model performance metrics (accuracy, precision\u002Frecall, IAA). Entry-level applicants welcome for junior labeling tracks.",{"h2":16,"desc":17},"Preferred Experience","Background in linguistics, cognitive science, or ML ops; prior work with content safety policies; experience in medical, legal, finance, or e-commerce taxonomies; exposure to LLM training pipelines; scripting for labeling automation; familiarity with dataset versioning, sampling, and auditor workflows; prior BPO or vendor-side annotation experience; multilingual capability for cross-locale datasets.",{"h2":19,"desc":20},"Workflows at Rex.zone","Join structured pipelines spanning data intake, guideline onboarding, pilot tasks, production annotation, tiered QA, and retrospective analysis. Use standardized instructions, gold sets, and calibration sessions to maintain training data quality. Participate in feedback loops that inform model performance improvement. Roles include annotator, reviewer, lead, domain specialist (NLP, computer vision, content safety), and RLHF evaluator, with clear progression paths.",{"h2":22,"desc":23},"Employment Types and Domains","Openings across remote, contract, freelance, full-time, part-time, intern, and temporary roles. Domains include NLP (NER, sentiment, intent), computer vision (bounding boxes, segmentation), content safety (policy-based labeling), LLM training (prompt evaluation, RLHF), and QA evaluation. Employers span AI labs, tech startups, BPOs, and annotation vendors operating through Rex.zone.",{"h2":25,"desc":26},"How to Apply","Create a Rex.zone profile, complete skill assessments, and select preferred domains (NLP, CV, content safety, RLHF). Submit work samples or complete a short pilot. Indicate availability (remote, contract, full-time) and seniority (entry-level, mid, senior). Successful candidates receive invitations to active projects and vendor rosters.",{"title":28,"content":29},"Candidate Q&A",[30,33,36,39],{"A":31,"Q":32},"You may use in-house Rex.zone tooling and common platforms for text, image, and video annotation, with productivity metrics and audit trails.","Which tools will I use?",{"A":34,"Q":35},"Yes. You can opt into multiple projects if your availability, domain skills, and quality scores meet requirements.","Can I work across multiple projects?",{"A":37,"Q":38},"Schedules vary by employer. Options include flexible hours for remote freelance, fixed shifts for vendor or BPO projects, and standard hours for full-time.","What are typical schedules?",{"A":40,"Q":41},"Not mandatory for entry-level labeling. Understanding AI\u002FML workflows, LLM evaluation, and QA processes improves candidacy for mid\u002Fsenior roles.","Do I need prior ML experience?","Data Annotation"]