Remote AI Jobs in Brazil

Remote AI jobs in Brazil at Rex.zone focus on search-recognizable AI training roles that improve large language model performance through training data quality, annotation guidelines compliance, RLHF, and QA evaluation. You will support LLM training pipelines by completing data labeling, prompt evaluation, named entity recognition, content safety labeling, and (when needed) computer vision annotation. These full-time remote roles connect Brazil-based talent with global AI labs, tech startups, BPOs, and annotation vendors hiring through Rex.zone.

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Open Role: Remote AI/ML Data Annotation & RLHF Specialist (Brazil)

Title: Remote AI/ML Data Annotation & RLHF Specialist (Brazil) 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: RLHF, data labeling, prompt evaluation, QA evaluation, LLM evaluation, named entity recognition, content safety labeling, annotation guidelines, training data quality Salary Currency: USD Salary Min: 63360 Salary Max: 126720 Pay Period: YEAR You will annotate and evaluate AI training data used to improve large language model behavior, instruction following, and safety. This role blends data labeling, RLHF-style preference ranking, and model response evaluation to drive model performance improvement across real product workflows. Responsibilities: - Perform data labeling for LLM training datasets, including intent labeling, classification, and structured extraction - Execute RLHF tasks such as preference ranking, rubric-based grading, and comparative evaluations - Conduct prompt evaluation and response evaluation for helpfulness, honesty, and harmlessness - Apply named entity recognition and entity linking guidelines for high-precision text annotation - Complete QA evaluation, audit sampled work, and resolve disagreements via calibration sessions - Follow annotation guidelines compliance standards and provide feedback to improve specs and edge-case handling - Support content safety labeling for policy categories (self-harm, hate, harassment, sexual content, illicit behavior) - Contribute to continuous improvement of training data quality, including error taxonomy and root-cause notes Required Qualifications: - 3+ years in data annotation, LLM evaluation, QA, trust & safety, or related AI data operations - Strong English reading comprehension and ability to apply detailed rubrics consistently - Experience with training data quality processes: inter-annotator agreement, sampling plans, and audit workflows - Comfortable working with ambiguity, documenting decisions, and escalating guideline gaps Preferred Qualifications: - Experience with RLHF, prompt engineering evaluation, or model evaluation frameworks - Familiarity with NLP tasks (NER, sentiment, intent, summarization) and dataset formatting - Exposure to computer vision annotation (bounding boxes, polygons) is a plus but not required What You’ll Work On: - Large language model evaluation across conversation, reasoning, and tool-use scenarios - Policy-aligned content safety labeling and safety evaluation sets - Training data operations that enable reliable model performance improvement How to Apply: - Apply through Rex.zone with a brief summary of relevant annotation, RLHF, and QA evaluation experience - Be prepared for a paid qualification task focused on annotation guidelines compliance and edge cases

How Rex.zone Supports Remote AI Jobs in Brazil

Rex.zone connects Brazil-based remote talent to full-time, contract, and freelance AI data operations work, including NLP labeling, LLM evaluation, RLHF-style ranking, content safety labeling, and computer vision annotation. You’ll see roles across AI labs, tech startups, BPOs, and annotation vendors with clear rubrics, measurable training data quality targets, and QA evaluation loops.

Key Workflows You’ll Use

Common workflows include: prompt evaluation, rubric scoring, pairwise ranking for RLHF, dataset curation, named entity recognition, content safety labeling, audit sampling, disagreement resolution, and annotation guidelines compliance checks. The goal is consistent training data quality that leads to model performance improvement in production LLM systems.

Role Modifiers and Related Remote AI Roles

You may also see modifier-based variants such as: remote, full-time, contract, freelance, entry-level, and senior. Related roles include: AI/ML data labeler, LLM evaluator, QA evaluator, content safety rater, NLP annotator, and computer vision annotation specialist.

Frequently Asked Questions

  • Q: What does “remote AI jobs in Brazil” mean on Rex.zone?

    It refers to remote AI data operations roles that can be performed from Brazil, commonly involving data labeling, RLHF-style ranking, prompt evaluation, and QA evaluation for LLM training pipelines, while the employer entity may be global.

  • Q: Are these roles only for software engineers?

    No. Many remote AI jobs in Brazil are focused on AI data operations: annotation, LLM evaluation, content safety labeling, named entity recognition, and training data quality QA, which often do not require traditional software engineering backgrounds.

  • Q: What skills matter most for these roles?

    Key skills include RLHF concepts, data labeling accuracy, prompt evaluation, QA evaluation methods, annotation guidelines compliance, training data quality auditing, named entity recognition, and content safety labeling.

  • Q: Is the work full-time and remote?

    This page targets full-time remote roles; however, Rex.zone may also list contract, freelance, entry-level, or senior variations depending on hiring needs.

  • Q: What types of AI domains are covered?

    Common domains include NLP and LLM evaluation, content safety, and sometimes computer vision annotation, depending on the dataset and project requirements.

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