[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"job-stem-engineering-jobs-brazil":3},{"Ques":4,"Slug":31,"Header":32,"job_category":54},{"title":5,"content":6},"Frequently Asked Questions",[7,10,13,16,19,22,25,28],{"A":8,"Q":9},"Yes. Remote Type is Remote, and the role is designed for candidates based in Brazil while working with global engineering and AI teams through Rex.zone.","Are these STEM engineering jobs in Brazil fully remote?",{"A":11,"Q":12},"Common work includes building data labeling tooling, automating QA evaluation, implementing RLHF and prompt evaluation workflows, and maintaining LLM training pipelines that improve training data quality and model performance improvement.","What kind of engineering work is most common in this role?",{"A":14,"Q":15},"Both. The job includes NLP workflows such as named entity recognition and prompt evaluation, plus computer vision annotation and content safety labeling depending on project needs.","Is this role focused on NLP, computer vision, or both?",{"A":17,"Q":18},"QA evaluation includes sampling and auditing labeled data, enforcing annotation guidelines compliance, calibrating reviewers, tracking defects, and ensuring datasets meet acceptance thresholds before entering large language model evaluation or training.","What does QA evaluation mean in an AI training context?",{"A":20,"Q":21},"RLHF is Reinforcement Learning from Human Feedback. Engineering support includes building interfaces for pairwise ranking and rubric scoring, managing evaluator quality controls, and integrating feedback outputs into LLM training pipelines.","What is RLHF and how does it relate to engineering here?",{"A":23,"Q":24},"Experience Level is Mid-Senior. You should be able to own systems, ship reliable tooling, and collaborate cross-functionally with data operations and research.","What experience level is required?",{"A":26,"Q":27},"Employment Type is FULL_TIME. The page may mention other modifiers like contract or freelance for search coverage, but this specific posting is full-time remote.","Is the employment type full-time or contract?",{"A":29,"Q":30},"Salary is listed in USD with Salary Min 63360 and Salary Max 126720, Pay Period YEAR.","What salary range is listed for this role?","stem engineering jobs brazil",{"desc":33,"title":34,"content":35},"These STEM engineering jobs in Brazil focus on building and evaluating AI\u002FML systems that rely on high-quality training data, RLHF feedback, and QA evaluation for large language models. At Rex.zone, you will collaborate with distributed engineering teams to design annotation tooling, prompt evaluation workflows, and data labeling quality systems that improve model performance. This remote, full-time role connects real-world engineering practices with AI training pipelines, including NLP, computer vision annotation, and content safety labeling. Explore and apply through Rex.zone to join projects that emphasize training data quality, annotation guidelines compliance, and measurable model performance improvement.","STEM Engineering Jobs in Brazil (Remote, Full-Time)",[36,39,42,45,48,51],{"h2":37,"desc":38},"STEM Engineering Jobs in Brazil (Remote) — Role Overview","Title: STEM Engineering Jobs 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: Software Engineering, Machine Learning Engineering, Data Labeling Tooling, RLHF Evaluation Systems, LLM Training Pipelines, Prompt Evaluation, QA Evaluation, NLP, Computer Vision Annotation, Content Safety Labeling\nSalary Currency: USD\nSalary Min: 63360\nSalary Max: 126720\nPay Period: YEAR",{"h2":40,"desc":41},"What You Will Do","Design and maintain scalable engineering workflows for data labeling and QA evaluation across NLP and computer vision annotation; build internal tools to support annotation guidelines compliance, reviewer calibration, and training data quality checks; implement RLHF and prompt evaluation interfaces, including rubric-based scoring, pairwise ranking, and evaluator quality controls; develop datasets and pipelines that feed large language model evaluation and LLM training pipelines; partner with product, data operations, and research teams to translate model failure analysis into new labeling tasks and improved evaluation coverage; measure impact using model performance improvement metrics, inter-annotator agreement, and defect rate tracking; contribute to content safety labeling systems that support policy enforcement and safe model outputs.",{"h2":43,"desc":44},"Required Qualifications","Mid-Senior engineering experience in software engineering or machine learning engineering; strong Python proficiency and familiarity with data pipelines, APIs, and workflow automation; experience with QA evaluation practices such as sampling, audits, and acceptance criteria; ability to design annotation tooling or evaluation systems for data labeling, NER, prompt evaluation, or RLHF; understanding of training data quality concepts, guideline design, and annotation edge cases; experience collaborating remotely across time zones and documenting technical decisions.",{"h2":46,"desc":47},"Preferred Qualifications","Experience supporting LLM training pipelines, large language model evaluation, or safety-focused evaluation; familiarity with NLP tasks (classification, NER, summarization, retrieval) and computer vision annotation tasks (bounding boxes, segmentation, keypoints); knowledge of content safety labeling taxonomies and policy-driven evaluation; experience building dashboards for evaluation quality, reviewer performance, and model regression detection.",{"h2":49,"desc":50},"Working Model and Location","Remote: This role is remote and designed for candidates located in Brazil. Collaboration is conducted asynchronously with scheduled overlap for team rituals. Employment Type remains FULL_TIME.",{"h2":52,"desc":53},"How to Apply on Rex.zone","Apply through Rex.zone with a resume highlighting engineering impact, data pipeline ownership, and any experience with data labeling, QA evaluation, RLHF, or prompt evaluation. Include links to relevant repositories or case studies demonstrating tooling, workflow automation, or evaluation system design.","Engineering"]