[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"job-stem-careers-brazil":3},{"Ques":4,"Slug":28,"Header":29,"job_category":80},{"title":5,"content":6},"Frequently Asked Questions",[7,10,13,16,19,22,25],{"A":8,"Q":9},"Yes. Remote Type is Remote and the role is designed for full-time remote work.","Is this role remote?",{"A":11,"Q":12},"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.","Why does a STEM engineering role include RLHF, data labeling, and LLM evaluation?",{"A":14,"Q":15},"Typical tasks include NLP labeling (classification, NER), computer vision annotation, content safety labeling, rubric-based grading, sampling for training data quality, and documenting edge cases for guideline updates.","What kinds of tasks are included in data labeling and QA evaluation?",{"A":17,"Q":18},"This position is FULL_TIME. Rex.zone may list contract, freelance, or part-time roles separately, but this posting is full-time.","Is this position full-time or contract\u002Ffreelance?",{"A":20,"Q":21},"The Experience Level is Mid-Senior, emphasizing independent execution, strong documentation, and consistent quality control in evaluation workflows.","What experience level is expected?",{"A":23,"Q":24},"You may contribute across NLP, computer vision annotation, named entity recognition, prompt evaluation, RLHF, and content safety labeling depending on project needs and your strengths.","What domains might I work in (NLP, computer vision, content safety)?",{"A":26,"Q":27},"The salary range is 63360 to 126720 USD, paid on a YEAR basis.","What is the salary range and pay period?","stem-careers-brazil",{"desc":30,"title":31,"content":32},"STEM Careers in Brazil at Rex.zone focus on engineering work that supports AI\u002FML training workflows, including data labeling, RLHF evaluation, QA evaluation, and prompt evaluation to improve large language model performance. In this full-time remote role, you will apply annotation guidelines compliance, training data quality checks, and model performance improvement practices across NLP, computer vision, named entity recognition, and content safety labeling. You will collaborate with distributed teams, contribute to LLM training pipelines, and help AI labs, tech startups, and annotation vendors ship reliable datasets and evaluations. Explore and apply through Rex.zone to join a high-impact engineering function.","STEM Careers in Brazil",[33,36,39,49,58,65,74],{"h2":34,"desc":35},"Job Overview","Keyword: STEM Careers Brazil — Job Title: STEM Careers in 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: STEM engineering, applied machine learning, LLM training pipelines, RLHF, data labeling, QA evaluation, prompt evaluation, NLP, named entity recognition, computer vision annotation, content safety labeling, annotation guidelines compliance\nSalary Currency: USD\nSalary Min: 63360\nSalary Max: 126720\nPay Period: YEAR",{"h2":37,"desc":38},"About the Role","You will operate in a remote engineering capacity supporting STEM-aligned initiatives connected to AI\u002FML data operations and evaluation. Your work may include building and improving labeling workflows, reviewing annotation guidelines, running QA evaluation and adjudication, performing prompt evaluation for LLM behavior, and contributing to RLHF data quality. You will help ensure training data quality, reduce ambiguity in instructions, and document edge cases that affect large language model evaluation and model performance improvement.",{"h2":40,"desc":41},"What You Will Do",[42,43,44,45,46,47,48],"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\u002Fharassment, privacy, and sensitive content categories)","Collaborate asynchronously with cross-functional stakeholders (engineering, ops, QA) to unblock delivery and improve throughput",{"h2":50,"desc":51},"Required Qualifications",[52,53,54,55,56,57],"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\u002For 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",{"h2":59,"desc":60},"Preferred Qualifications",[61,62,63,64],"Experience with large language model evaluation methodologies (rubric grading, pairwise ranking, preference data collection)","Prior work with content safety labeling or trust & safety evaluation","Knowledge of inter-annotator agreement, sampling strategies, and quality control design","Exposure to vendor\u002FBPO annotation operations or AI lab evaluation pipelines",{"h2":66,"desc":67},"Work Model and Employment Details",[68,69,70,71,72,73],"Remote Type: Remote (must remain Remote)","Employment Type: FULL_TIME","Experience Level: Mid-Senior","Industry: Technology","Job Function: Engineering","Compensation Range: 63360 to 126720 USD per year",{"h2":75,"desc":76},"How to Apply on Rex.zone",[77,78,79],"Prepare a resume highlighting STEM engineering outcomes, AI\u002FML workflow exposure, and evaluation or labeling experience","Emphasize experience with training data quality, annotation guidelines compliance, and large language model evaluation","Apply via Rex.zone and include examples of structured documentation (rubrics, QA checklists, defect reports) when available","Engineering"]