AI Training Jobs in Brazil

AI training jobs in Brazil at Rex.zone focus on improving large language model and computer vision systems through data labeling, RLHF, prompt evaluation, and training data quality workflows. These remote, full-time roles support annotation guidelines compliance, QA evaluation, and model performance improvement across NLP, content safety labeling, and LLM training pipelines. Explore Rex.zone opportunities aligned to Brazilian talent while collaborating with global AI labs, tech startups, and annotation vendors to deliver reliable datasets, scalable evaluation, and measurable quality metrics for production AI.

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AI Training Jobs in Brazil

Keyword: AI Training Jobs in Brazil — Title: AI Training Specialist (Brazil, Remote) 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, data labeling, RLHF, prompt evaluation, LLM evaluation, QA evaluation, annotation guidelines, training data quality, named entity recognition, computer vision annotation, content safety labeling, LLM training pipelines Salary Currency: USD Salary Min: 63360 Salary Max: 126720 Pay Period: YEAR

About the Role

You will contribute to AI training workflows that turn raw text, images, and model outputs into high-quality supervised datasets and evaluation signals. Day to day work includes data labeling, RLHF preference ranking, prompt evaluation, QA evaluation, and guideline-based review to improve large language model behavior and model performance improvement. You will collaborate asynchronously with global teams via Rex.zone, applying consistent annotation guidelines compliance, running targeted audits, and reporting quality metrics that influence LLM training pipelines across NLP, computer vision, and content safety labeling use cases.

What You’ll Do

Core responsibilities include: creating and applying annotation taxonomies; labeling text and image data; performing named entity recognition and document classification; conducting RLHF comparisons and ranking tasks; executing prompt evaluation and rubric-based scoring; completing QA evaluation with adjudication and error analysis; validating training data quality via sampling and audits; identifying ambiguous edge cases and proposing guideline updates; tracking inter-annotator agreement and defect rates; documenting decisions to support consistent model evaluation and dataset versioning.

Domains You May Support

Depending on project needs, you may work on NLP evaluation, instruction following checks, safety policy classification, content moderation signals, hallucination and factuality assessment, multilingual Portuguese/English tasks, computer vision annotation (bounding boxes, segmentation, keypoints), OCR validation, or retrieval-augmented generation evaluation. Projects may be aligned to AI labs, tech startups, BPOs, or annotation vendors consuming outputs through Rex.zone.

Requirements

Mid-senior capability in structured evaluation work and operational rigor. Experience with data labeling or QA evaluation in production settings; comfort with rubric-based scoring and ambiguity resolution; strong written communication for guideline interpretation; ability to track and improve training data quality metrics; familiarity with LLM evaluation concepts (toxicity, helpfulness, factuality, instruction following) and RLHF-style preference tasks; detail orientation and consistent throughput in remote workflows.

Preferred Qualifications

Experience supporting LLM training pipelines end to end; prior work with named entity recognition or computer vision annotation tools; background in content safety labeling policies; exposure to prompt engineering and prompt evaluation; experience running audit programs, calibration sessions, or adjudication; familiarity with dataset versioning, sampling plans, and error taxonomy design; Portuguese/English bilingual proficiency for multilingual evaluation tasks.

Quality and Performance Expectations

Success is measured by annotation guidelines compliance, audit pass rates, inter-annotator agreement, defect escape rate, and turnaround time. You will be expected to maintain consistent quality under changing requirements, provide actionable feedback to refine rubrics, and support model performance improvement by surfacing recurring failure modes in model outputs.

Compensation

Salary Range: 63360 to 126720 USD per year. Compensation may vary by project scope, evaluation complexity, and demonstrated performance in QA evaluation and RLHF tasks.

How to Apply on Rex.zone

Apply through Rex.zone to be matched with remote, full-time AI training jobs in Brazil-aligned talent pools. Keep your profile focused on AI training, data labeling, RLHF evaluation, prompt evaluation, QA evaluation, and domain experience in NLP, computer vision annotation, and content safety labeling.

Frequently Asked Questions

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

    It refers to roles where Brazil-based or Brazil-aligned talent supports AI/ML training workflows (data labeling, RLHF, prompt evaluation, and QA evaluation) for LLMs and computer vision models through Rex.zone’s remote job pipeline.

  • Q: Is this role remote and full-time?

    Yes. The role is explicitly Remote and FULL_TIME, with asynchronous collaboration and quality-driven delivery expectations.

  • Q: What types of tasks are included?

    Common tasks include training data quality checks, annotation guidelines compliance, named entity recognition, prompt evaluation, RLHF preference ranking, QA evaluation, and content safety labeling for large language model evaluation.

  • Q: Which domains are most common?

    NLP and LLM training pipelines are common, along with computer vision annotation and content safety labeling. Assignments can vary by employer type including AI labs, tech startups, BPOs, and annotation vendors.

  • Q: What skills should I highlight to match this keyword intent?

    Emphasize AI training, data labeling, RLHF, prompt evaluation, LLM evaluation, QA evaluation, training data quality, annotation guidelines compliance, named entity recognition, computer vision annotation, and content safety labeling.

  • Q: How is quality measured?

    Quality is typically assessed using audit accuracy, rubric adherence, inter-annotator agreement, defect rates, calibration outcomes, and consistency across edge cases impacting model performance improvement.

  • Q: Do I need engineering experience for Job Function: Engineering?

    You do not need to be a software developer, but you should be comfortable with structured evaluation, operational rigor, and technical communication that supports production AI/ML pipelines.

  • Q: What is RLHF and why is it part of the work?

    RLHF (Reinforcement Learning from Human Feedback) uses human preference signals to improve model behavior. In practice, you may compare model outputs, rank responses, and apply rubrics that guide training objectives.

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50+Countries Represented

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