[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"job-most-in-demand-stem-jobs-united-states":3},{"Ques":4,"Slug":25,"Header":26,"job_category":48},{"title":5,"content":6},"Frequently Asked Questions",[7,10,13,16,19,22],{"A":8,"Q":9},"Common high-demand STEM roles include Machine Learning Engineer, Data Scientist, Software Engineer, Cybersecurity Engineer, Cloud Engineer, and Data Engineer. Demand is driven by cloud adoption, security needs, and AI\u002FML productization, including LLM training pipelines and model evaluation.","What are the most in demand STEM jobs in the United States?",{"A":11,"Q":12},"Yes. Each role on this page is listed as Remote and FULL_TIME in the LinkedIn-compatible job metadata.","Are these roles remote and full-time?",{"A":14,"Q":15},"Many engineering teams now rely on human-in-the-loop workflows to improve model performance, including RLHF, data labeling, QA evaluation, prompt evaluation, named entity recognition, computer vision annotation, and content safety labeling. These workflows increase demand for engineers who can build reliable data and evaluation pipelines.","How do AI\u002FML concepts like RLHF and data labeling relate to STEM hiring demand?",{"A":17,"Q":18},"Highlight job-relevant skills such as Python, SQL, system design, cloud platforms (AWS\u002FAzure\u002FGCP), MLOps, security fundamentals, distributed systems, experiment design, and model evaluation. If applying to AI\u002FML-adjacent roles, emphasize training data quality, annotation guidelines compliance, and large language model evaluation.","What skills should I highlight to be competitive for these STEM jobs?",{"A":20,"Q":21},"Apply via Rex.zone by selecting the role that best matches your experience and skills, then following the application workflow for that posting.","Where do I apply for these roles?",{"A":23,"Q":24},"Rex.zone may list remote contract or freelance roles in addition to full-time roles. This page focuses on Remote FULL_TIME postings, but you can search Rex.zone using modifiers like contract, freelance, entry-level, or senior to find additional options.","Do you offer contract or freelance options too?","most-in-demand-stem-jobs-united-states",{"desc":27,"title":28,"content":29},"Explore remote, full-time STEM roles in the United States on Rex.zone, focused on high-demand engineering and AI\u002FML workflows. These roles map to search-recognizable job entities (e.g., software engineer, data scientist, machine learning engineer, cybersecurity engineer, cloud engineer) and connect directly to real production pipelines: large language model evaluation, RLHF, training data quality, data labeling, QA evaluation, prompt evaluation, named entity recognition, computer vision annotation, and content safety labeling. If you build, test, secure, or scale AI systems, this page helps you match skills to hiring demand and apply through Rex.zone.","Most In Demand STEM Jobs in the United States",[30,33,36,39,42,45],{"h2":31,"desc":32},"Machine Learning Engineer","LinkedIn Job Metadata: Title + Machine Learning Engineer | 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 + Machine learning, Deep learning, LLM training pipelines, RLHF, Model evaluation, Prompt evaluation, Python, PyTorch, TensorFlow, MLOps, Data labeling, QA evaluation | Salary Currency + USD | Salary Min + 63360 | Salary Max + 126720 | Pay Period + YEAR\n\nRole Overview: Build and deploy ML systems that improve model performance in real-world products. You will design training and evaluation workflows, integrate human feedback signals (RLHF) where applicable, and partner with data operations to ensure training data quality.\n\nKey Responsibilities: [\"Design, train, and tune models for NLP and computer vision use cases\", \"Define offline and online evaluation strategies to measure model performance improvement\", \"Collaborate with annotation teams on annotation guidelines compliance and label taxonomy\", \"Implement prompt evaluation and QA evaluation loops for LLM behavior\", \"Improve dataset curation, data labeling throughput, and error analysis\", \"Ship models using MLOps practices (CI\u002FCD, monitoring, drift detection)\"]\n\nPreferred Background: [\"Experience with LLMs, RLHF, and large language model evaluation\", \"Strong Python and deep learning frameworks (PyTorch\u002FTensorFlow)\", \"Hands-on experience with training data pipelines, feature stores, and model monitoring\", \"Familiarity with NER, content safety labeling, or computer vision annotation\"]",{"h2":34,"desc":35},"Data Scientist","LinkedIn Job Metadata: Title + Data Scientist | 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 science, Statistical modeling, Experiment design, A\u002FB testing, SQL, Python, Machine learning, Model evaluation, Data quality, NLP analytics, Training data quality, QA evaluation | Salary Currency + USD | Salary Min + 63360 | Salary Max + 126720 | Pay Period + YEAR\n\nRole Overview: Own analytical problem-solving across product and ML initiatives, connecting data quality to measurable outcomes. You will help define evaluation metrics, validate model improvements, and support LLM training pipelines with robust measurement.\n\nKey Responsibilities: [\"Develop statistical models and dashboards to track model and product KPIs\", \"Design experiments (A\u002FB tests) and interpret impact on user outcomes\", \"Partner with ML engineers to create model evaluation and error analysis plans\", \"Audit training data quality and identify label noise or sampling bias\", \"Support NLP evaluation, prompt evaluation, and QA evaluation reporting\", \"Translate findings into actionable recommendations for engineering teams\"]\n\nPreferred Background: [\"Strong SQL and Python analytics\", \"Experience with causal inference or experiment design\", \"Familiarity with ML evaluation metrics and dataset auditing\", \"Exposure to annotation workflows, NER, or content safety labeling is a plus\"]",{"h2":37,"desc":38},"Software Engineer","LinkedIn Job Metadata: Title + Software Engineer | 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 + Software engineering, Distributed systems, APIs, Cloud infrastructure, Python, Java, System design, Data pipelines, MLOps, Observability, QA automation, Security best practices | Salary Currency + USD | Salary Min + 63360 | Salary Max + 126720 | Pay Period + YEAR\n\nRole Overview: Build reliable services and platforms that power modern ML and product systems. You will enable data pipelines, evaluation tooling, and scalable infrastructure used by AI\u002FML teams and data operations.\n\nKey Responsibilities: [\"Design and implement backend services and APIs for data and model workflows\", \"Build scalable data pipelines supporting training and evaluation workloads\", \"Improve system design, reliability, and observability (logs, metrics, tracing)\", \"Integrate QA automation and testing strategies for production readiness\", \"Collaborate with ML teams on MLOps integrations and deployment patterns\", \"Harden services with security best practices and access controls\"]\n\nPreferred Background: [\"Experience with distributed systems and cloud-native development\", \"Strong coding in Python\u002FJava and service ownership\", \"Familiarity with MLOps concepts and ML evaluation tooling\", \"Exposure to annotation platforms or model evaluation pipelines is a plus\"]",{"h2":40,"desc":41},"Cybersecurity Engineer","LinkedIn Job Metadata: Title + Cybersecurity Engineer | 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 + Cybersecurity, Threat modeling, Incident response, Cloud security, IAM, Security monitoring, Vulnerability management, Zero trust, Secure SDLC, SIEM, Compliance, Risk assessment | Salary Currency + USD | Salary Min + 63360 | Salary Max + 126720 | Pay Period + YEAR\n\nRole Overview: Protect systems, data, and ML platforms with modern security engineering practices. You will secure cloud environments, reduce risk in the software lifecycle, and strengthen monitoring and incident response.\n\nKey Responsibilities: [\"Implement threat modeling and security architecture reviews\", \"Build and tune security monitoring (SIEM) and alerting pipelines\", \"Lead incident response, postmortems, and remediation tracking\", \"Harden IAM, secrets management, and access control policies\", \"Drive vulnerability management and secure SDLC practices\", \"Partner with engineering to meet compliance and risk requirements\"]\n\nPreferred Background: [\"Strong cloud security and IAM experience\", \"Hands-on incident response and detection engineering\", \"Familiarity with zero trust and secure SDLC\", \"Experience supporting AI\u002FML platforms is a plus\"]",{"h2":43,"desc":44},"Cloud Engineer","LinkedIn Job Metadata: Title + Cloud Engineer | 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 + Cloud engineering, AWS, Azure, GCP, Infrastructure as code, Kubernetes, CI\u002FCD, Networking, Observability, Cost optimization, Security best practices, Data pipelines | Salary Currency + USD | Salary Min + 63360 | Salary Max + 126720 | Pay Period + YEAR\n\nRole Overview: Build and operate cloud infrastructure that supports scalable applications and ML workloads. You will enable reliable environments for training data pipelines, model evaluation, and production services.\n\nKey Responsibilities: [\"Design and manage cloud infrastructure across compute, storage, and networking\", \"Automate provisioning using infrastructure as code and CI\u002FCD\", \"Operate Kubernetes and containerized services for scalable workloads\", \"Implement observability and SRE practices (SLIs\u002FSLOs, on-call readiness)\", \"Optimize cloud cost and performance for data and ML workloads\", \"Apply security best practices across cloud environments\"]\n\nPreferred Background: [\"Hands-on experience in AWS\u002FAzure\u002FGCP\", \"Kubernetes and IaC expertise\", \"Experience supporting data pipelines or MLOps infrastructure\", \"Strong debugging, reliability, and cost optimization skills\"]",{"h2":46,"desc":47},"Data Engineer","LinkedIn Job Metadata: Title + Data Engineer | 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 engineering, ETL, SQL, Python, Data pipelines, Data modeling, Spark, Warehousing, Data quality, Metadata management, Training data quality, Annotation workflows | Salary Currency + USD | Salary Min + 63360 | Salary Max + 126720 | Pay Period + YEAR\n\nRole Overview: Build data pipelines that power analytics and AI. You will improve training data quality, enable scalable ingestion and transformation, and support annotation workflows that feed LLM training pipelines.\n\nKey Responsibilities: [\"Develop ETL\u002FELT pipelines for structured and unstructured data\", \"Implement data quality checks, anomaly detection, and lineage tracking\", \"Model data for warehouse\u002Flakehouse consumption and ML features\", \"Support dataset curation for ML teams, including sampling and deduplication\", \"Enable metadata management and governance for sensitive data\", \"Collaborate with annotation teams on data labeling inputs and outputs\"]\n\nPreferred Background: [\"Strong SQL and Python; experience with Spark\", \"Hands-on pipeline orchestration and warehouse\u002Flakehouse design\", \"Experience with training data quality and dataset versioning\", \"Familiarity with annotation workflows or NER is a plus\"]","Engineering"]