Entry Level STEM Jobs Brazil
Rex.zone · United States
- On site
- Full-time
- United States
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About the role
Entry Level STEM Jobs Brazil (Remote, Full-Time)
Join Rex.zone to support real-world AI/ML training workflows by producing and validating high-quality training data for large language model evaluation and LLM training pipelines. You will follow annotation guidelines compliance, document rationales, and help drive model performance improvement across NLP and computer vision tasks.
What You Will Do
$30–$50 per hour (base salary).
Remote (distributed team).
Join Rex.zone to support real-world AI/ML training workflows by producing and validating high-quality training data for large language model evaluation and LLM training pipelines. You will follow annotation guidelines compliance, document rationales, and help drive model performance improvement across NLP and computer vision tasks.
What You Will Do
- Perform data labeling for NLP and computer vision annotation tasks using clear taxonomies and labeling tools
- Complete RLHF evaluation by ranking outputs, selecting preferences, and writing concise justifications
- Run QA evaluation passes to detect label noise, guideline drift, and ambiguity in prompt evaluation setups
- Support named entity recognition by applying consistent span boundaries and categories
- Contribute to content safety labeling for policy-aligned datasets
- Track common failure modes to improve training data quality and reduce rework
- Collaborate asynchronously with reviewers, project leads, and engineering stakeholders
- STEM foundation (engineering, CS, math, physics, statistics, data science, or related)
- Ability to read detailed instructions and apply rules consistently under QA evaluation
- Strong written communication for explaining RLHF choices and prompt evaluation rationales
- Attention to detail; basic proficiency with spreadsheets, web tools, or annotation platforms
- Ability to work full-time remotely with reliable internet
$30–$50 per hour (base salary).
Remote (distributed team).