[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"job-entry-level-stem-jobs-brazil":3},{"Ques":4,"Slug":31,"Header":32,"job_category":88},{"title":5,"content":6},"Frequently Asked Questions",[7,10,13,16,19,22,25,28],{"A":8,"Q":9},"Yes. The position is explicitly Remote and FULL_TIME, aligned to supporting distributed AI\u002FML training workflows.","Is this role remote and full-time?",{"A":11,"Q":12},"The page targets the SEO keyword \"Entry Level STEM Jobs Brazil\" while keeping the provided default metadata unchanged, including Experience Level: Mid-Senior.","Why does the page say Entry Level but the experience level is Mid-Senior?",{"A":14,"Q":15},"You will perform data labeling, RLHF evaluation, QA evaluation, and prompt evaluation to improve training data quality for LLM training pipelines, including NLP, named entity recognition, computer vision annotation, and content safety labeling.","What kind of tasks will I do in AI\u002FML data operations?",{"A":17,"Q":18},"RLHF evaluation typically involves comparing multiple model outputs, choosing the better response, and documenting why, using a rubric that supports large language model evaluation and model performance improvement.","What is RLHF evaluation in practical terms?",{"A":20,"Q":21},"Careful reading, consistency with annotation guidelines compliance, strong written reasoning, and comfort with structured QA evaluation processes are critical, along with familiarity with data labeling and prompt evaluation.","What skills are most important for success?",{"A":23,"Q":24},"AI labs, tech startups, BPOs, and annotation vendors commonly hire for data labeling, RLHF, and model evaluation work that feeds LLM training pipelines.","What types of employers use this kind of role?",{"A":26,"Q":27},"It is helpful but not required. Many tasks are guideline-driven; a STEM background and strong attention to training data quality are often sufficient to start.","Do I need prior experience with NLP or computer vision?",{"A":29,"Q":30},"QA evaluation includes reviewing labeled items for accuracy and consistency, checking edge cases, identifying label noise, and ensuring annotation guidelines compliance to protect downstream model performance.","What does QA evaluation mean here?","entry-level-stem-jobs-brazil",{"desc":33,"title":34,"content":35},"Entry Level STEM Jobs Brazil at Rex.zone connect early-career STEM talent with real-world AI\u002FML training workflows, including data labeling, RLHF evaluation, QA evaluation, and prompt evaluation for large language models. In this remote, full-time track, you will support LLM training pipelines by following annotation guidelines compliance, improving training data quality, and contributing to model performance improvement across NLP, computer vision annotation, named entity recognition, and content safety labeling. Explore Rex.zone roles aligned with STEM fundamentals—statistics, programming, and structured problem-solving—while building practical experience with scalable data operations for AI labs, tech startups, and annotation vendors.","Entry Level STEM Jobs Brazil",[36,39,42,52,60,67,74,82],{"h2":37,"desc":38},"Entry Level STEM Jobs Brazil — LinkedIn Job Metadata","Title: Entry Level STEM Jobs 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: Data Labeling, RLHF Evaluation, QA Evaluation, Prompt Evaluation, Named Entity Recognition, Computer Vision Annotation, Content Safety Labeling, LLM Training Pipelines | Salary Currency: USD | Salary Min: 63360 | Salary Max: 126720 | Pay Period: YEAR",{"h2":40,"desc":41},"About the Role","You will work on AI\u002FML data operations that transform raw text, images, and structured inputs into high-quality training data. Daily work includes data labeling, RLHF preference judgments, QA evaluation, and prompt evaluation to support large language model evaluation and model performance improvement. You will apply annotation guidelines compliance, handle edge cases, document rationales, and participate in calibration to keep training data quality consistent across tasks in NLP, computer vision, and content safety.",{"h2":43,"desc":44},"What You Will Do",[45,46,47,48,49,50,51],"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 and entity linking by applying consistent span boundaries and categories","Contribute to content safety labeling for policy-aligned datasets (e.g., harassment, self-harm, adult content)","Track common failure modes to improve annotation guidelines compliance and reduce rework","Collaborate asynchronously with reviewers, project leads, and engineering stakeholders in LLM training pipelines",{"h2":53,"desc":54},"Required Qualifications",[55,56,57,58,59],"STEM foundation (e.g., engineering, computer science, math, physics, statistics, data science, or related)","Comfort reading technical instructions and applying rules consistently under QA evaluation","Strong written communication for explaining RLHF choices and prompt evaluation rationales","Basic proficiency with spreadsheets, web tools, or annotation platforms; attention to detail","Ability to work full-time in a remote environment with reliable internet and consistent availability",{"h2":61,"desc":62},"Preferred Qualifications",[63,64,65,66],"Exposure to NLP, machine learning, or LLM concepts (tokens, prompts, hallucinations, safety, evaluation)","Experience with named entity recognition, document labeling, or computer vision annotation","Familiarity with dataset versioning, inter-annotator agreement, or training data quality practices","Interest in AI product quality, model evaluation, and content safety labeling",{"h2":68,"desc":69},"Tools and Workflows You Will Use",[70,71,72,73],"Annotation tools for text and image labeling, including review queues and QA sampling","Rubrics for RLHF evaluation, prompt evaluation, and large language model evaluation","Guideline-driven workflows: calibration, adjudication, re-labeling, and error taxonomy tracking","Structured reporting to support LLM training pipelines and model performance improvement",{"h2":75,"desc":76},"Compensation and Work Details",[77,78,79,80,81],"Remote Type: Remote","Employment Type: FULL_TIME","Salary Range: 63360 to 126720 USD per YEAR","Industry: Technology","Job Function: Engineering",{"h2":83,"desc":84},"How to Apply on Rex.zone",[85,86,87],"Search this page for \"Entry Level STEM Jobs Brazil\" and review the requirements and skills match","Prepare a resume highlighting STEM coursework, rule-following work, QA habits, and any NLP\u002FCV projects","Apply through Rex.zone and be ready for a short evaluation focused on training data quality and consistency","Engineering"]