[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"job-ai-data-labeling-jobs-brazil":3},{"Ques":4,"Slug":31,"Header":32,"job_category":60},{"title":5,"content":6},"Frequently Asked Questions",[7,10,13,16,19,22,25,28],{"A":8,"Q":9},"Yes. This posting is explicitly Remote and FULL_TIME, aligned with distributed annotation operations and evaluation workflows.","Are these roles remote and full-time?",{"A":11,"Q":12},"Tasks commonly include data labeling\u002Fdata annotation, RLHF preference ranking, prompt evaluation, QA evaluation, named entity recognition, content safety labeling, and computer vision annotation, all contributing to training data quality.","What types of tasks are included in AI data labeling jobs?",{"A":14,"Q":15},"Your labeled data and evaluations feed LLM training pipelines and offline\u002Fonline testing, improving large language model evaluation signals, reducing failure modes, and driving model performance improvement.","How does this work support LLMs and AI models?",{"A":17,"Q":18},"Not always, but it is helpful. Strong annotation guidelines compliance, QA evaluation discipline, and the ability to apply rubrics consistently are core requirements for RLHF and prompt evaluation tasks.","Do I need prior RLHF experience?",{"A":20,"Q":21},"Projects can span NLP, named entity recognition, content safety labeling, and computer vision annotation, depending on client domain and dataset needs.","What domains might I label for?",{"A":23,"Q":24},"This page targets the keyword intent for AI data labeling jobs Brazil; eligibility can vary by project and client requirements. Check Rex.zone listings for location and compliance constraints.","Is this only for Brazil-based candidates?",{"A":26,"Q":27},"AI labs, tech startups, BPOs, and annotation vendors commonly hire for remote data labeling, RLHF, and QA evaluation roles.","What employer types typically hire for this work?",{"A":29,"Q":30},"Some Rex.zone listings may be contract or freelance, but this specific posting is FULL_TIME. Use filters on Rex.zone to find contract, freelance, entry-level, or senior options.","Are contract or freelance options available?","ai-data-labeling-jobs-brazil",{"desc":33,"title":34,"content":35},"AI Data Labeling Jobs Brazil on Rex.zone focus on training data creation for AI\u002FML systems, including data labeling, RLHF preference ranking, QA evaluation, and prompt evaluation to improve large language model evaluation and computer vision models. You will apply annotation guidelines compliance, produce training data quality at scale, and support model performance improvement across NLP, named entity recognition, content safety labeling, and LLM training pipelines. This is a Remote, FULL_TIME opportunity aligned with production annotation workflows used by AI labs, tech startups, BPOs, and annotation vendors—explore and apply on Rex.zone.","AI Data Labeling Jobs Brazil",[36,39,42,45,48,51,54,57],{"h2":37,"desc":38},"AI Data Labeling Jobs Brazil (Remote, Full-Time)","Title: AI Data Labeling Jobs 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: AI data labeling, data annotation, RLHF, prompt evaluation, LLM evaluation, QA evaluation, training data quality, annotation guidelines compliance, named entity recognition, computer vision annotation, content safety labeling\nSalary Currency: USD\nSalary Min: 63360\nSalary Max: 126720\nPay Period: YEAR",{"h2":40,"desc":41},"About the Role","You will deliver high-quality labeled datasets that support LLM training pipelines and multimodal model development. Typical work includes RLHF preference ranking, prompt evaluation for instruction-following, and QA evaluation against annotation guidelines to ensure training data quality and model performance improvement. Projects may span NLP classification, named entity recognition, content safety labeling, and computer vision annotation (bounding boxes, polygons, keypoints) depending on client needs.",{"h2":43,"desc":44},"What You Will Do","Key responsibilities include: (1) Label text, image, and\u002For multimodal data following detailed annotation guidelines compliance, (2) Perform RLHF tasks such as preference ranking and helpfulness\u002Fharmlessness evaluations for large language model evaluation, (3) Execute QA evaluation via spot checks, consensus review, and error categorization to improve training data quality, (4) Conduct prompt evaluation and rubric-based scoring to support instruction tuning, (5) Document edge cases, create clarifications, and provide feedback loops that drive model performance improvement.",{"h2":46,"desc":47},"Workstreams You May Support","Common workstreams include: (1) NLP labeling (intent, sentiment, toxicity, topic, summarization quality), (2) Named entity recognition and entity linking, (3) RLHF and human preference data collection, (4) Content safety labeling for policy compliance and risk mitigation, (5) Computer vision annotation (detection\u002Fsegmentation), (6) LLM evaluation tasks such as grounding checks, reasoning quality ratings, and refusal\u002Fsafety assessments.",{"h2":49,"desc":50},"Required Qualifications","You should have experience delivering data annotation or QA evaluation in production workflows. You can follow annotation guidelines compliance precisely, maintain training data quality, and communicate edge cases clearly. You are comfortable with rubric-based scoring, prompt evaluation, and structured labeling schemas. You can operate independently in Remote settings and meet throughput and accuracy targets.",{"h2":52,"desc":53},"Preferred Qualifications","Preferred: exposure to RLHF, large language model evaluation, content safety labeling, named entity recognition, or computer vision annotation. Familiarity with inter-annotator agreement, sampling-based QA evaluation, error analysis, and iterative guideline updates is a plus. Experience supporting AI labs, tech startups, BPOs, or annotation vendors is helpful.",{"h2":55,"desc":56},"Quality, Metrics, and Tooling","You will be measured on training data quality, annotation guidelines compliance, accuracy, consistency, and throughput. QA evaluation may include gold tasks, audits, adjudication, and calibration sessions. Tooling varies by project and can include web-based labeling interfaces, review queues, and structured taxonomies for LLM evaluation and content safety labeling.",{"h2":58,"desc":59},"How to Apply on Rex.zone","Apply through Rex.zone to access current AI data labeling jobs Brazil postings, project requirements, and onboarding steps. Keep your profile updated with relevant skills (RLHF, prompt evaluation, QA evaluation, named entity recognition, computer vision annotation) and highlight prior work improving training data quality and model performance improvement.","AI Data Operations"]