[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"job-ai-data-labeling-jobs-united-states":3},{"Ques":4,"Slug":25,"Header":26,"job_category":88},{"title":5,"content":6},"Frequently Asked Questions",[7,10,13,16,19,22],{"A":8,"Q":9},"Yes. This posting is for US-based candidates and is explicitly marked Remote, with full-time employment.","Are these AI data labeling jobs remote in the United States?",{"A":11,"Q":12},"Common tasks include data labeling for text and images, RLHF preference ranking, prompt evaluation, QA evaluation, named entity recognition, computer vision annotation, and content safety labeling to improve training data quality.","What kinds of tasks are included in AI data labeling?",{"A":14,"Q":15},"RLHF (Reinforcement Learning from Human Feedback) uses human preference data to guide model behavior. In this role, you may rank responses, apply rubrics, and produce high-quality preference labels that improve model performance and alignment.","What is RLHF and why is it part of this role?",{"A":17,"Q":18},"QA evaluation includes audits against gold sets, reviewer calibration, consistency checks, and guideline compliance reviews to ensure training data quality and reduce label noise that can degrade model performance.","What does QA evaluation mean for labeling teams?",{"A":20,"Q":21},"Not always. Some projects focus on NLP annotation like named entity recognition and prompt evaluation, while others include computer vision annotation. The role favors candidates who can adapt to multiple domains.","Do I need experience in NLP or computer vision to apply?",{"A":23,"Q":24},"Rex.zone is the platform where you can explore the job listing, review requirements, and apply for the remote US AI data labeling role.","How does Rex.zone fit into the application process?","ai-data-labeling-jobs-united-states",{"desc":27,"title":28,"content":29},"AI Data Labeling jobs in the United States focus on creating high-quality training data for modern AI\u002FML systems, including large language model evaluation, RLHF workflows, and QA evaluation for model performance improvement. On Rex.zone, you will support real-world LLM training pipelines by following annotation guidelines compliance, producing consistent labels, and validating training data quality across NLP, computer vision annotation, and content safety labeling tasks. This remote, full-time role is designed for US-based professionals who can apply policy-driven judgment, maintain reviewer consistency, and document edge cases that impact model behavior. Explore and apply through Rex.zone to join teams powering production AI systems.","AI Data Labeling Jobs in the United States",[30,56,67,76,84],{"h2":28,"desc":31},[32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55],"Title: AI Data Labeling Specialist (United States)","Date: 25-02-2026","Company: Rexzone","Country: US","Remote Type: Remote","Employment Type: FULL_TIME","Experience Level: Mid-Senior","Industry: Technology","Job Function: Engineering","Skills: AI data labeling, training data quality, annotation guidelines compliance, RLHF, LLM evaluation, QA evaluation, prompt evaluation, NLP annotation, named entity recognition, computer vision annotation, content safety labeling, dataset auditing","Salary Currency: USD","Salary Min: 63360","Salary Max: 126720","Pay Period: YEAR","Entity: AI Data Labeling Specialist for LLM Training and Evaluation","Core Workflows: Data labeling, RLHF ranking, prompt evaluation, QA evaluation, training data quality audits","Domains: NLP, named entity recognition, computer vision annotation, content safety labeling","Role Summary: Deliver consistent labels and evaluations that improve model performance, reduce model regressions, and strengthen large language model evaluation signals in production AI\u002FML training pipelines on Rex.zone.","Key Responsibilities: Follow annotation guidelines compliance, label text and image data, perform RLHF preference ranking, run QA evaluation checks, document ambiguous cases, flag policy and safety issues, support dataset versioning and annotation guideline updates.","Quality Standards: Inter-annotator agreement, consistency checks, gold set accuracy, edge-case documentation, calibration sessions, audit trails for model training data.","Tools and Formats: Web-based annotation tools, spreadsheet-based audits, JSON-based schemas, prompt\u002Fresponse evaluation interfaces, taxonomy and label ontology maintenance.","Who This Role Fits: Mid-senior annotators and evaluators with strong written judgment, experience with LLM evaluation, comfort with ambiguity, and ability to maintain high training data quality under production timelines.","Search Modifiers Covered: Remote, full-time, contract, freelance, entry-level, senior; NLP, computer vision, content safety, LLM training; AI labs, tech startups, BPOs, annotation vendors.","How to Apply: Visit Rex.zone and apply to the US remote AI data labeling job listing. Prepare a short summary of prior annotation work, QA evaluation experience, and examples of guideline-driven decisions.",{"h2":57,"desc":58},"What You’ll Do (Day-to-Day)",[59,60,61,62,63,64,65,66],"Perform data labeling for text, conversations, and multimodal samples to support LLM training pipelines","Execute RLHF tasks such as pairwise ranking and preference labeling to improve model alignment","Conduct prompt evaluation and response quality grading using rubric-based scoring","Run QA evaluation checks, identify disagreement drivers, and propose guideline clarifications","Apply named entity recognition and span labeling for NLP datasets when required","Support computer vision annotation tasks (bounding boxes, polygons, attributes) when applicable","Complete content safety labeling for harmful, sensitive, or policy-violating content categories","Maintain annotation guidelines compliance, track edge cases, and write clear reviewer notes",{"h2":68,"desc":69},"Requirements",[70,71,72,73,74,75],"US-based and eligible to work in the United States","Mid-senior experience in AI data labeling, data annotation, or LLM evaluation programs","Ability to follow detailed rubrics and sustain high training data quality over time","Strong written English comprehension for prompt evaluation, reasoning review, and policy interpretation","Experience with QA evaluation methods (gold sets, audits, calibration, disagreement resolution)","Comfort working remotely with secure workflows and time-bound production queues",{"h2":77,"desc":78},"Preferred Qualifications",[79,80,81,82,83],"Hands-on experience with RLHF (pairwise ranking, preference data, rubric scoring)","Experience with named entity recognition, intent classification, or structured NLP labeling","Experience with computer vision annotation and visual taxonomy design","Familiarity with content safety labeling, trust and safety policies, or moderation standards","Experience working with AI labs, tech startups, BPOs, or annotation vendors",{"h2":85,"desc":86},"Compensation",[87,36,37],"Salary Range (USD, Year): 63360 - 126720","AI Data Operations"]