[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"job-most-in-demand-stem-jobs-brazil":3},{"Ques":4,"Slug":28,"Header":29,"job_category":96},{"title":5,"content":6},"Frequently Asked Questions",[7,10,13,16,19,22,25],{"A":8,"Q":9},"It refers to STEM roles that are consistently in high demand in Brazil’s market (software, data, ML, cloud, cybersecurity, CV, NLP) and are commonly offered as remote, full-time positions on Rex.zone for teams building modern AI systems.","What does “most in demand STEM jobs Brazil” mean on Rex.zone?",{"A":11,"Q":12},"Yes. Each role on this page is explicitly listed as Remote and FULL_TIME, matching common search modifiers like remote and full-time.","Are these roles remote and full-time?",{"A":14,"Q":15},"Many in-demand STEM roles now intersect with LLM training pipelines, including RLHF evaluation, prompt evaluation, QA evaluation, data labeling, named entity recognition, computer vision annotation, and content safety labeling to improve training data quality and model performance.","How do these jobs connect to AI\u002FML workflows like RLHF and data labeling?",{"A":17,"Q":18},"Not always, but mid-senior candidates are expected to learn and apply evaluation practices such as rubric scoring, offline metrics, and QA evaluation loops, especially when working with large language model evaluation and training data quality.","Do I need prior experience with LLM evaluation to apply?",{"A":20,"Q":21},"AI labs, tech startups, enterprises, BPOs, annotation vendors, and platform teams commonly hire for these roles, especially for building data pipelines, secure infrastructure, and evaluation systems.","What employer types hire for these roles?",{"A":23,"Q":24},"This page is configured for FULL_TIME roles, but it includes common modifiers (contract, freelance, entry-level, senior) to match how candidates search; Rex.zone may list additional employment types on other pages.","Does the page cover contract or freelance roles too?",{"A":26,"Q":27},"Pick based on your strongest skill cluster: ML Engineer (modeling + MLOps), Data Engineer (pipelines + quality), Software Engineer (services + systems), Cloud Engineer (infra + automation), Cybersecurity Engineer (secure SDLC + incident response), CV Engineer (vision + CV annotation), NLP Engineer (language + LLM evaluation).","How should I choose which role fits me best?","most in demand stem jobs brazil",{"desc":30,"title":31,"content":32},"This page highlights the most in demand STEM jobs in Brazil and how they map to real-world AI\u002FML delivery work on Rex.zone, including LLM training pipelines, RLHF evaluation, data labeling, QA evaluation, and production engineering. These remote, full-time roles reflect the skills employers seek most—software engineering, data engineering, machine learning, cloud, cybersecurity, and computer vision—while emphasizing training data quality, annotation guidelines compliance, and model performance improvement. Explore roles, review LinkedIn-ready metadata, and apply through Rex.zone to join teams building and evaluating modern AI systems.","Most In Demand STEM Jobs Brazil",[33,42,51,60,69,78,87],{"h2":34,"desc":35,"desc_list":36},"Machine Learning Engineer","Title: Machine Learning Engineer\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: Machine Learning, LLM Training Pipelines, Model Evaluation, RLHF, Prompt Evaluation, Feature Engineering, Python, MLOps\nSalary Currency: USD\nSalary Min: 63360\nSalary Max: 126720\nPay Period: YEAR\n\nBuild, train, and evaluate ML systems used in product features and AI\u002FML training workflows. You will improve model performance through data-centric iteration, offline\u002Fonline evaluation, and training data quality practices. Work closely with data labeling teams to define annotation guidelines compliance, leverage RLHF signals where appropriate, and run prompt evaluation and QA evaluation loops for large language model evaluation. Typical projects include ranking models, classifiers, retrieval augmentation metrics, and model monitoring in production.",[37,38,39,40,41],"Design end-to-end ML solutions (data, training, evaluation, deployment).","Define evaluation frameworks for LLMs: prompt evaluation, rubric scoring, and QA evaluation.","Partner with annotation vendors and internal labelers on data labeling strategy and quality.","Implement model monitoring, drift detection, and retraining triggers.","Collaborate with product and engineering to ship reliable ML services.",{"h2":43,"desc":44,"desc_list":45},"Data Engineer","Title: Data Engineer\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: Data Pipelines, ETL, SQL, Python, Spark, Data Warehousing, Airflow, Data Quality\nSalary Currency: USD\nSalary Min: 63360\nSalary Max: 126720\nPay Period: YEAR\n\nOwn scalable data pipelines that power analytics, ML features, and LLM training pipelines. You will build reliable ETL\u002FELT processes, enforce training data quality, and create datasets used for data labeling, named entity recognition, and large-scale evaluation. This role supports AI\u002FML teams by ensuring clean, well-documented data assets, lineage, and auditability for QA evaluation and model performance improvement.",[46,47,48,49,50],"Build and maintain batch\u002Fstream pipelines with strong data quality guarantees.","Model datasets for ML training, RLHF feedback storage, and evaluation reporting.","Implement data validation, observability, and cost-aware processing.","Partner with ML teams to version datasets and support reproducible experiments.","Create documentation and governance workflows for secure access.",{"h2":52,"desc":53,"desc_list":54},"Software Engineer","Title: Software Engineer\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: Backend Development, APIs, Distributed Systems, Python, Java, Microservices, Testing, Cloud\nSalary Currency: USD\nSalary Min: 63360\nSalary Max: 126720\nPay Period: YEAR\n\nBuild production systems that serve AI products and the tooling behind LLM training pipelines. You will implement services for data ingestion, annotation workflow management, QA evaluation dashboards, and model inference at scale. The work often intersects with data labeling platforms, content safety labeling queues, and large language model evaluation pipelines that require strong reliability engineering and high test coverage.",[55,56,57,58,59],"Design and ship APIs and microservices for AI\u002FML product features and internal tooling.","Implement workflow orchestration for annotation, review, and evaluation operations.","Improve system performance, reliability, and observability in distributed environments.","Collaborate with ML and data teams to integrate model inference and evaluation signals.","Write automated tests and enforce secure coding standards.",{"h2":61,"desc":62,"desc_list":63},"Cybersecurity Engineer","Title: Cybersecurity Engineer\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: Security Engineering, Threat Modeling, Incident Response, IAM, Cloud Security, SIEM, Vulnerability Management, Secure SDLC\nSalary Currency: USD\nSalary Min: 63360\nSalary Max: 126720\nPay Period: YEAR\n\nProtect platforms and AI\u002FML pipelines by embedding security into infrastructure, applications, and data operations. You will secure datasets used in data labeling and large language model evaluation, implement access controls for RLHF feedback and prompt evaluation logs, and strengthen secure SDLC practices. This role supports compliance, risk reduction, and operational resilience across remote teams.",[64,65,66,67,68],"Run threat modeling and harden services used for annotation and evaluation workflows.","Build detections and response playbooks for suspicious activity and data exfiltration.","Implement IAM best practices, secrets management, and least-privilege access.","Perform vulnerability management and security reviews for critical services.","Partner with engineering to automate security controls in CI\u002FCD.",{"h2":70,"desc":71,"desc_list":72},"Cloud Engineer","Title: Cloud Engineer\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: Cloud Infrastructure, AWS, Terraform, Kubernetes, CI\u002FCD, Observability, Networking, Cost Optimization\nSalary Currency: USD\nSalary Min: 63360\nSalary Max: 126720\nPay Period: YEAR\n\nBuild and operate cloud infrastructure that supports ML training, LLM training pipelines, and high-throughput data labeling operations. You will provide scalable compute, secure networking, and observability for evaluation services including RLHF evaluation, prompt evaluation, and QA evaluation workloads. This role focuses on reliability, automation, and cost optimization for remote-first engineering teams.",[73,74,75,76,77],"Provision and manage cloud environments with Infrastructure as Code (IaC).","Operate Kubernetes and CI\u002FCD pipelines for model services and tooling.","Implement monitoring, alerting, and SLOs for critical AI\u002FML systems.","Optimize cloud costs for compute-heavy evaluation and training workloads.","Harden networking and identity controls for secure remote operations.",{"h2":79,"desc":80,"desc_list":81},"Computer Vision Engineer","Title: Computer Vision Engineer\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: Computer Vision, CV Annotation, Image Labeling, Object Detection, Segmentation, PyTorch, Model Evaluation, Data Labeling\nSalary Currency: USD\nSalary Min: 63360\nSalary Max: 126720\nPay Period: YEAR\n\nDevelop and evaluate computer vision models and the data pipelines that make them accurate. You will define CV annotation strategies (bounding boxes, polygons, segmentation masks), enforce annotation guidelines compliance, and improve training data quality through review and QA evaluation. Work includes model performance improvement via dataset curation, active learning loops, and robust offline evaluation for detection and segmentation systems.",[82,83,84,85,86],"Train and evaluate CV models for detection, segmentation, and classification tasks.","Design data labeling workflows and sampling strategies to improve training data quality.","Implement QA evaluation and inter-annotator agreement checks for labeled datasets.","Collaborate with data operations on annotation tooling and guideline updates.","Track metrics and run experiments to drive model performance improvement.",{"h2":88,"desc":89,"desc_list":90},"NLP Engineer","Title: NLP Engineer\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: NLP, LLM Evaluation, Named Entity Recognition, Text Classification, RLHF, Prompt Evaluation, Python, Data Labeling\nSalary Currency: USD\nSalary Min: 63360\nSalary Max: 126720\nPay Period: YEAR\n\nBuild NLP systems and evaluation methods for large language models and classic language tasks. You will design annotation schemas for named entity recognition, content safety labeling, and text classification, then use QA evaluation and prompt evaluation to measure quality. This role improves LLM training pipelines by iterating on datasets, leveraging RLHF where appropriate, and establishing reproducible evaluation that correlates with real user outcomes.",[91,92,93,94,95],"Develop NLP\u002FLLM features and evaluation tooling across multiple domains.","Create labeling taxonomies and guidelines for NER, safety, and intent classification.","Run large language model evaluation with rubric-based scoring and QA checks.","Partner with data ops to improve annotation guidelines compliance and consistency.","Analyze errors and propose data-centric fixes for model performance improvement.","Engineering"]