[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"job-data-annotation-remote-jobs":3},{"Slug":4,"Header":5,"Ques":33,"job_category":45},"data-annotation-remote-jobs",{"desc":6,"title":7,"content":8},"Data annotation specialist is a search-recognizable role that labels and evaluates data powering AI\u002FML training at scale. On Rex.zone, you’ll work across RLHF (Reinforcement Learning from Human Feedback), data labeling, QA evaluation, prompt evaluation, named entity recognition, computer vision annotation, and content safety labeling to improve training data quality and model performance. Apply for remote, contract, freelance, and full-time openings with AI labs, tech startups, BPOs, and annotation vendors. Join LLM training pipelines, ensure annotation guidelines compliance, and deliver measurable model performance improvement through rigorous evaluation and feedback loops—all through Rex.zone.","Data Annotation Remote Jobs",[9,12,15,18,21,24,27,30],{"h2":10,"desc":11},"Key Responsibilities","• Produce high-quality labels for NLP, computer vision, and speech datasets, adhering to annotation guidelines compliance and quality thresholds. • Execute RLHF and prompt evaluation tasks (pairwise preference, scoring rubrics, safety\u002Fharms checks) to drive model performance improvement. • Perform NER, sentiment, intent, taxonomy\u002Fontology mapping, and content safety labeling (PII, toxicity, bias, misinformation). • Conduct computer vision annotation: bounding boxes, polygons, keypoints, semantic\u002Finstance segmentation, and video event labeling. • Run QA evaluation: inter-annotator agreement checks, gold set validation, error taxonomies, and calibration sessions. • Maintain training data quality with sampling, spot checks, and issue triage in Jira\u002FAsana. • Document edge cases, update label schemas, and collaborate with ML engineers on feedback cycles.",{"h2":13,"desc":14},"Required Qualifications","• Experience in data labeling or QA evaluation for AI\u002FML datasets (entry-level welcome; senior roles require 2–5+ years). • Strong attention to detail, reading comprehension, and consistency under clear SLAs. • Familiarity with LLM evaluation tasks (pairwise preference, scoring prompts), basic ML metrics (precision, recall, F1), and inter-annotator agreement (Cohen’s kappa). • Domain literacy in NLP or computer vision annotation; ability to follow evolving guidelines. • Excellent written English; multilingual skills are a plus.",{"h2":16,"desc":17},"Preferred Skills","• Experience with RLHF data generation, red teaming, safety taxonomies, and prompt engineering. • Knowledge of ontology design, label schema versioning, and dataset governance. • Understanding of active learning, human-in-the-loop, and evaluation harnesses. • Basic scripting for data ops (CSV\u002FJSON hygiene) is a plus.",{"h2":19,"desc":20},"Tools and Stack","• Labeling: Label Studio, Prodigy, Scale AI, SuperAnnotate, CVAT, SageMaker Ground Truth. • Collaboration: Jira, Notion, Confluence, Slack. • Versioning\u002FStorage: DVC, git, data lakes. • Evaluation: custom LLM judges, pairwise preference UIs, rubric-based scoring forms.",{"h2":22,"desc":23},"Workflows You’ll Join","• LLM training pipelines: data collection → annotation → RLHF preference → safety review → model eval → iteration. • Computer vision labeling sprints with QA gates and ground-truth audits. • Content safety labeling for policy compliance and risk mitigation. • Continuous quality improvement via gold sets, spot checks, and calibration.",{"h2":25,"desc":26},"Employment Types and Modifiers","Remote-first roles with options for full-time, part-time, contract, freelance, and temporary. Entry-level to senior tracks available across NLP, computer vision, content safety, and LLM training. Employers include AI labs, tech startups, BPOs, and annotation vendors.",{"h2":28,"desc":29},"Compensation and Benefits","Competitive hourly rates or salaries depending on seniority and domain complexity, with performance-based bonuses tied to training data quality and delivery SLAs. Some roles include benefits, equipment stipends, and flexible schedules across time zones.",{"h2":31,"desc":32},"How to Apply on Rex.zone","Create your profile on Rex.zone, highlight domain expertise (NLP, CV, safety), and include sample projects demonstrating annotation guidelines compliance and QA evidence. Complete skill checks (NER, prompt evaluation, CV tasks) to qualify faster for remote openings.",{"title":34,"content":35},"Quick Answers",[36,39,42],{"A":37,"Q":38},"No, but basic familiarity with datasets, CSV\u002FJSON hygiene, and evaluation concepts helps you progress faster.","Do I need ML coding experience?",{"A":40,"Q":41},"Global. Many projects are async with optional overlap for QA reviews and calibration sessions.","What time zones are supported?",{"A":43,"Q":44},"Most roles include onboarding with guidelines, gold sets, and calibration tasks before production work.","Is training provided?","Data Annotation & Labeling"]