[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"job-ai-trainer-jobs-in-brazil":3},{"Ques":4,"Slug":31,"Header":32,"job_category":63},{"title":5,"content":6},"Frequently Asked Questions",[7,10,13,16,19,22,25,28],{"A":8,"Q":9},"An AI Trainer creates and reviews training signals such as data labeling, RLHF preference rankings, and rubric-based evaluations to improve model behavior, including instruction following, reasoning quality, and content safety outcomes.","What does an AI Trainer do in LLM projects?",{"A":11,"Q":12},"Yes. The role is explicitly Remote and FULL_TIME.","Is this role remote and full-time?",{"A":14,"Q":15},"The SEO keyword targets AI Trainer jobs in Brazil, while the job metadata Country field remains US as provided in the defaults. The role is remote and can support Brazil-aligned hiring needs while keeping the specified metadata unchanged.","Why does the page say Brazil while the country field is US?",{"A":17,"Q":18},"The most relevant skills include RLHF, prompt evaluation, data labeling, QA evaluation, training data quality, annotation guidelines compliance, LLM evaluation, content safety labeling, named entity recognition, and NLP.","What skills matter most for AI Trainer jobs in Brazil?",{"A":20,"Q":21},"In addition to labeling, you may perform QA evaluation, adjudication, calibration, prompt evaluation, disagreement analysis, guideline updates, and targeted reviews for high-risk content safety categories.","What kinds of tasks are included besides labeling?",{"A":23,"Q":24},"Some projects may include computer vision annotation or multimodal evaluation, but the core focus is typically NLP and LLM evaluation workflows.","Does the role involve computer vision annotation?",{"A":26,"Q":27},"RLHF work often includes pairwise ranking, scoring responses against rubrics, and writing short rationales that help align model outputs with quality and safety targets.","How does RLHF relate to daily work?",{"A":29,"Q":30},"Apply via Rex.zone by locating the AI Trainer jobs in Brazil page and submitting your application for the remote, full-time role.","Where do I apply?","ai-trainer-jobs-in-brazil",{"desc":33,"title":34,"content":35},"AI Trainer jobs in Brazil focus on improving large language models through RLHF, prompt evaluation, training data quality checks, and annotation guidelines compliance. On Rex.zone, you will support end-to-end LLM training pipelines by labeling and reviewing text, conversation turns, and multimodal inputs, performing QA evaluation, and documenting decision rationales that drive model performance improvement. This remote, full-time role is designed for mid-senior professionals who can apply consistent rubrics, handle content safety labeling, and collaborate with engineers to translate real-world user intent into reliable training signals for NLP and, when needed, computer vision annotation workflows.","AI Trainer Jobs in Brazil",[36,39,42,45,48,51,54,57,60],{"h2":37,"desc":38},"Job Heading: AI Trainer Jobs in Brazil","Title: AI Trainer Jobs in Brazil\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, prompt evaluation, data labeling, QA evaluation, training data quality, annotation guidelines compliance, LLM evaluation, content safety labeling, named entity recognition, NLP\nSalary Currency: USD\nSalary Min: 63360\nSalary Max: 126720\nPay Period: YEAR",{"h2":40,"desc":41},"About the Role","You will act as an AI Trainer supporting LLM training pipelines by producing high-quality labels and preference data. Work includes RLHF tasks (pairwise ranking and rubric-based scoring), prompt evaluation, QA evaluation of model outputs, and root-cause analysis for recurring failure modes. You will follow annotation guidelines compliance requirements, create consistent rationales, and help improve training data quality through audits, disagreement resolution, and calibration sessions.",{"h2":43,"desc":44},"What You Will Do","Key responsibilities include: (1) Perform data labeling for NLP tasks such as intent classification, summarization checks, factuality review, and named entity recognition; (2) Execute RLHF and preference ranking to optimize helpfulness, honesty, and harmlessness; (3) Conduct prompt evaluation and response quality scoring using detailed rubrics; (4) Run QA evaluation, track error categories, and propose guideline updates; (5) Support content safety labeling for policy-sensitive topics; (6) Collaborate with engineering partners to validate edge cases, sampling strategies, and quality thresholds.",{"h2":46,"desc":47},"Core Workstreams","You will rotate across multiple workstreams depending on project needs: LLM evaluation (instruction following, reasoning quality, refusal behavior), training data quality audits (spot checks, adjudication, gold set creation), and annotation operations (throughput planning, calibration, and reviewer alignment). Some programs may include computer vision annotation or multimodal evaluation when text+image tasks are required.",{"h2":49,"desc":50},"Required Qualifications","Mid-senior experience in data operations, evaluation, QA, or applied AI workflows. Strong written communication and ability to produce consistent decision rationales. Proven attention to detail with rubric-based scoring and annotation guidelines compliance. Familiarity with NLP concepts, LLM behavior, prompt evaluation, and training data quality practices.",{"h2":52,"desc":53},"Preferred Qualifications","Hands-on experience with RLHF or preference modeling tasks. Experience with named entity recognition, content safety labeling, or policy-driven moderation datasets. Exposure to computer vision annotation or multimodal evaluation. Ability to analyze evaluator disagreement and propose actionable guideline changes that lead to model performance improvement.",{"h2":55,"desc":56},"Quality, Tooling, and Collaboration","You will work in structured annotation and evaluation tools, follow audit trails, and meet quality targets using gold data and inter-annotator agreement methods. Collaboration includes participating in calibration sessions, documenting edge cases, and partnering with engineering to ensure datasets and rubrics produce reliable training signals.",{"h2":58,"desc":59},"Employment Details","Remote, FULL_TIME position with Rexzone, categorized under Technology with Job Function in Engineering. Compensation range is USD 63360 to 126720 per year, depending on scope, performance, and project assignment.",{"h2":61,"desc":62},"How to Apply on Rex.zone","Visit Rex.zone to explore AI Trainer jobs in Brazil and submit your application for the remote, full-time opening. Ensure your resume highlights RLHF, prompt evaluation, QA evaluation, data labeling, and training data quality work that aligns with LLM training pipelines.","AI Data Operations"]