[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"job-ai-trainer-jobs-in-united-states":3},{"Ques":4,"Slug":25,"Header":26,"job_category":99},{"title":5,"content":6},"Frequently Asked Questions",[7,10,13,16,19,22],{"A":8,"Q":9},"AI Trainer jobs in the United States are roles where you help improve AI systems by creating labeled training data and evaluating model outputs. Typical work includes RLHF preference judgments, prompt evaluation, QA evaluation, and content safety labeling within LLM training pipelines.","What are AI Trainer jobs in the United States?",{"A":11,"Q":12},"Yes. This posting is marked Remote and FULL_TIME, aligned to the metadata shown under the job heading.","Is this role remote and full-time?",{"A":14,"Q":15},"AI Trainers commonly support NLP and LLM evaluation, and may also work on computer vision annotation and content safety labeling depending on the project.","Which AI domains do AI Trainers usually support?",{"A":17,"Q":18},"Core skills include AI training, RLHF, LLM evaluation, prompt evaluation, data labeling, QA evaluation, annotation guidelines, content safety labeling, training data quality, and model performance improvement.","What skills are most important for AI Trainer roles?",{"A":20,"Q":21},"Many AI Trainer roles are operational and evaluation-focused, but they still benefit from engineering-style rigor: structured reasoning, reproducible decisions, careful QA, and comfort with iterative evaluation workflows.","Do I need engineering experience for AI Trainer jobs?",{"A":23,"Q":24},"Rex.zone is the platform context for discovering and applying to AI training and evaluation roles, where your work contributes to training data quality and measurable improvements in model performance.","How does Rex.zone fit into the workflow?","ai-trainer-jobs-in-united-states",{"desc":27,"title":28,"content":29},"AI Trainer jobs in the United States focus on improving model behavior by creating and evaluating training data for real-world AI\u002FML workflows. On Rex.zone, AI Trainers support large language model evaluation, RLHF, prompt evaluation, data labeling, and QA evaluation so AI systems become safer, more accurate, and more helpful. You will apply annotation guidelines compliance, judge outputs against rubrics, and identify failure modes that impact model performance improvement. This is a Remote, FULL_TIME role built for people who can combine language precision, analytical thinking, and quality-first execution across NLP, content safety labeling, and LLM training pipelines.","AI Trainer Jobs in the United States",[30,33,36,39,50,59,67,75,85,92],{"h2":31,"desc":32},"Job Heading: AI Trainer Jobs in the United States","Title: AI Trainer Jobs in the 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 training, RLHF, LLM evaluation, prompt evaluation, data labeling, QA evaluation, annotation guidelines, content safety labeling, training data quality, model performance improvement | Salary Currency: USD | Salary Min: 63360 | Salary Max: 126720 | Pay Period: YEAR",{"h2":34,"desc":35},"About Rex.zone","Rex.zone connects AI\u002FML teams with professionals who build training datasets, run evaluations, and improve model quality. We support projects across AI labs, tech startups, annotation vendors, and BPO-style data operations teams, with workflows spanning NLP, computer vision, and content policy evaluation.",{"h2":37,"desc":38},"About the Role","As an AI Trainer, you will evaluate and refine model outputs using structured rubrics, produce high-quality labeled data, and provide feedback aligned to RLHF-style preference signals. You will work in LLM training pipelines that require careful prompt evaluation, reasoning trace review (when applicable), and iterative QA evaluation to drive training data quality and model performance improvement.",{"h2":40,"desc":41},"Key Responsibilities",[42,43,44,45,46,47,48,49],"Perform RLHF-style ranking and preference judgments to improve assistant helpfulness, harmlessness, and honesty","Execute prompt evaluation and response grading using calibrated scoring rubrics and edge-case test prompts","Create and validate labeled datasets for NLP tasks such as classification, summarization, and named entity recognition","Conduct QA evaluation to ensure annotation guidelines compliance and consistent label quality across batches","Flag ambiguous instructions, propose rubric clarifications, and document decision rationales for reviewers","Identify systematic model failure patterns (hallucinations, bias, refusal errors, safety gaps) and report actionable insights","Support content safety labeling for policy categories, sensitive topics, and disallowed content detection","Collaborate with cross-functional partners to refine evaluation frameworks, gold sets, and acceptance criteria",{"h2":51,"desc":52},"What You Will Work On",[53,54,55,56,57,58],"Large language model evaluation and response quality measurement","RLHF data collection and preference modeling support","Training data quality audits and adjudication workflows","Named entity recognition and structured information extraction labeling","Content safety labeling and policy-aligned classification","Optional project tracks: computer vision annotation (bounding boxes, segmentation) depending on assignment",{"h2":60,"desc":61},"Required Qualifications",[62,63,64,65,66],"Mid-Senior experience in AI training, data labeling, QA evaluation, or LLM evaluation roles","Strong written communication and the ability to apply rubrics consistently across many examples","Demonstrated attention to detail in annotation guidelines compliance and audit-ready documentation","Comfort working with ambiguous model behavior and iterating based on reviewer feedback","Ability to meet throughput targets without sacrificing training data quality",{"h2":68,"desc":69},"Preferred Qualifications",[70,71,72,73,74],"Hands-on exposure to RLHF, reward modeling concepts, or pairwise ranking methodologies","Experience with prompt evaluation for instruction-following, tool use, or multi-turn conversations","Familiarity with content safety labeling taxonomies and policy reasoning","Experience supporting NLP datasets (classification, NER, summarization) or computer vision annotation","Interest in evaluation design, gold dataset creation, and inter-annotator agreement measurement",{"h2":76,"desc":77},"Employment Details",[78,79,80,81,82,83,84],"Country: US","Remote Type: Remote","Employment Type: FULL_TIME","Experience Level: Mid-Senior","Industry: Technology","Job Function: Engineering","Salary Range: 63360 to 126720 USD per YEAR",{"h2":86,"desc":87},"How to Apply on Rex.zone",[88,89,90,91],"Create or update your Rex.zone profile with relevant AI training, RLHF, and evaluation experience","Highlight projects involving training data quality, annotation guidelines compliance, and QA evaluation","Submit your application to this Remote, FULL_TIME AI Trainer opportunity","Be prepared for a short skills calibration (rubric scoring, prompt evaluation, or labeling exercise)",{"h2":93,"desc":94},"Search Modifiers and Related Roles",[95,96,97,98],"Remote AI Trainer jobs, FULL_TIME AI Trainer roles, contract AI trainer, freelance AI trainer, entry-level AI trainer, senior AI trainer","NLP evaluation, LLM training pipelines, RLHF, prompt evaluation, QA evaluation, data labeling, named entity recognition","Computer vision annotation, content safety labeling, policy evaluation, model performance improvement","Employer types: AI labs, tech startups, annotation vendors, BPO data operations teams","AI Data Operations"]