[{"data":1,"prerenderedAt":49},["ShallowReactive",2],{"job-ai-jobs-in-united-states":3},{"Ques":4,"Slug":25,"Header":26,"job_category":48},{"title":5,"content":6},"Frequently Asked Questions",[7,10,13,16,19,22],{"A":8,"Q":9},"It refers to US-based roles supporting AI\u002FML training workflows, commonly focused on data labeling, RLHF, prompt evaluation, QA evaluation, and content safety labeling for LLM training pipelines.","What does “AI jobs in the United States” mean on Rex.zone?",{"A":11,"Q":12},"Yes. The role is explicitly Remote and based in the United States.","Is this role remote?",{"A":14,"Q":15},"Typical tasks include data labeling, named entity recognition, computer vision annotation, RLHF preference ranking, prompt evaluation, content safety labeling, adjudication, and quality audits tied to training data quality.","What kind of tasks are included in AI data operations?",{"A":17,"Q":18},"Emphasize RLHF, data labeling, QA evaluation, prompt evaluation, named entity recognition, computer vision annotation, content safety labeling, and experience supporting LLM training pipelines.","What skills should I emphasize to match this posting?",{"A":20,"Q":21},"The role targets Mid-Senior candidates who can independently follow guidelines, handle edge cases, and contribute to evaluation quality and error analysis.","What experience level is expected?",{"A":23,"Q":24},"Yes. Salary is listed in USD with YEAR pay period and salary numbers shown without symbols or commas, following the provided ranges.","Is the compensation information included and consistent?","ai-jobs-in-united-states",{"desc":27,"title":28,"content":29},"AI jobs in the United States on Rex.zone focus on practical AI\u002FML production work that improves model performance through training data quality, RLHF, and evaluation. These remote, full-time roles support large language model evaluation, data labeling, prompt evaluation, and QA evaluation workflows used by AI labs, tech startups, annotation vendors, and BPO teams. You will apply annotation guidelines compliance, content safety labeling, and structured feedback to strengthen LLM training pipelines across NLP and computer vision tasks. Explore and apply to Rex.zone roles aligned to real-world AI training and quality operations across the US.","AI Jobs in the United States",[30,33,36,39,42,45],{"h2":31,"desc":32},"AI Data Annotation Specialist (United States, Remote)","Title: AI Data Annotation Specialist (United States, Remote)\nDate: 25-02-2026\nCompany: Rexzone\nCountry: US\nRemote Type: Remote\nEmployment Type: FULL_TIME\nExperience Level: Mid-Senior\nIndustry: Technology\nJob Function: Engineering\nSkills: RLHF, data labeling, QA evaluation, prompt evaluation, named entity recognition, computer vision annotation, content safety labeling, LLM training pipelines\nSalary Currency: USD\nSalary Min: 63360\nSalary Max: 126720\nPay Period: YEAR",{"h2":34,"desc":35},"What You Will Do","Execute data labeling for NLP and computer vision datasets using detailed annotation guidelines; perform RLHF preference ranking and write high-signal rationales for LLM alignment; run prompt evaluation and QA evaluation to detect hallucinations, instruction-following failures, and policy issues; complete content safety labeling for toxicity, self-harm, hate, and sexual content categories; perform named entity recognition (NER) and span-level tagging for entities, relations, and attributes; conduct adjudication, error analysis, and disagreement resolution to improve annotation consistency; deliver training data quality reports that connect labeling outcomes to model performance improvement; collaborate with operations and engineering on task design, taxonomy updates, and LLM training pipeline requirements.",{"h2":37,"desc":38},"Required Qualifications","Mid-senior experience in annotation, evaluation, or QA workflows for AI\u002FML datasets; strong reading comprehension and structured writing for rationale-based RLHF and evaluation; ability to follow annotation guidelines compliance with high accuracy and low rework; familiarity with NLP concepts such as intent, sentiment, NER, and summarization quality; familiarity with computer vision annotation types (bounding boxes, polygons, keypoints) is a plus; comfort working with ambiguity, edge cases, and policy-driven content safety labeling; ability to track quality metrics (accuracy, agreement rate, defect rate) and perform basic error analysis.",{"h2":40,"desc":41},"Nice to Have","Experience with large language model evaluation frameworks and rubric design; prior work with prompt evaluation for instruction following, tool use, and reasoning tasks; experience supporting AI labs, tech startups, annotation vendors, or BPO delivery teams; exposure to multilingual evaluation, domain-specific taxonomies, or safety policy enforcement; familiarity with dataset versioning and audit trails for training data quality.",{"h2":43,"desc":44},"Working Model, Location, and Modifiers","Remote: This role is Remote within the United States; Employment type: FULL_TIME; Common modifiers supported on Rex.zone: remote, full-time, contract, freelance, entry-level, senior; Domains you may encounter: NLP, computer vision, content safety, LLM training; Employer types across the ecosystem: AI labs, tech startups, annotation vendors, and BPO teams.",{"h2":46,"desc":47},"How to Apply on Rex.zone","Apply through Rex.zone with a resume highlighting AI\u002FML data operations experience, RLHF or evaluation work, and examples of annotation guidelines compliance. Include any relevant work on training data quality, prompt evaluation, content safety labeling, NER, or computer vision annotation that demonstrates reliable QA evaluation and model performance improvement impact.","AI Data Operations",1786024776775]