STEM Careers in Brazil

About The Role

STEM Careers in Brazil at Rex.zone support AI/ML training workflows and large language model (LLM) evaluation. In this full-time remote engineering role, you will help improve model performance through high-quality training data, RLHF evaluation, and rigorous QA processes.

What You Will Do

• Support LLM training pipelines by validating datasets, rubrics, and evaluation protocols used in RLHF and offline evaluation
• Perform data labeling and QA evaluation across NLP, named entity recognition (NER), and computer vision annotation tasks
• Execute prompt evaluation and response grading to improve helpfulness, correctness, and policy compliance
• Apply annotation guidelines compliance checks, identify inconsistencies, and propose guideline clarifications
• Track training data quality metrics (agreement rates, defect taxonomy, escalation patterns) and recommend fixes
• Contribute to content safety labeling workflows (toxicity, self-harm, hate/harassment, privacy, and sensitive content categories)
• Collaborate asynchronously with cross-functional stakeholders (engineering, ops, QA) to unblock delivery and improve throughput

Required Qualifications

• Mid-Senior experience in a STEM discipline (engineering, computer science, data, or related technical field) or equivalent practical experience
• Hands-on experience with at least two of: data labeling, QA evaluation, prompt evaluation, RLHF workflows, or dataset auditing
• Strong written communication for producing clear guidelines, rubrics, and defect reports
• Working familiarity with NLP concepts (classification, NER, information extraction) and/or computer vision annotation
• Ability to follow strict annotation guidelines compliance while applying sound judgment to ambiguous edge cases
• Experience collaborating remotely across time zones with consistent delivery in full-time schedules

FAQ

• Is this role remote? Yes. Remote Type is Remote and the role is designed for full-time remote work.
• Why does a STEM engineering role include RLHF, data labeling, and LLM evaluation? Modern AI systems depend on high-quality training data and robust evaluation. This role supports LLM training pipelines through RLHF datasets, prompt evaluation, QA evaluation, and annotation guidelines compliance to drive model performance improvement.

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