[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"job-highest-paying-stem-jobs-united-states":3},{"Ques":4,"Slug":22,"Header":23,"job_category":42},{"title":5,"content":6},"Frequently Asked Questions",[7,10,13,16,19],{"A":8,"Q":9},"Yes. Each job listed is marked Remote and FULL_TIME in the LinkedIn-compatible metadata, with Country set to US.","Are these remote, full-time STEM jobs in the United States?",{"A":11,"Q":12},"High-paying modern STEM roles increasingly support AI\u002FML training workflows where RLHF, training data quality, prompt evaluation, and QA evaluation directly drive model performance improvement in production systems.","Why do these roles emphasize RLHF, data labeling, and LLM evaluation?",{"A":14,"Q":15},"All roles are set to Mid-Senior to match the page intent and the provided default Experience Level.","What experience level is targeted for these jobs?",{"A":17,"Q":18},"The roles span NLP (including named entity recognition), computer vision annotation, content safety labeling, and end-to-end LLM training pipelines with evaluation and QA.","What kinds of domains are covered by these STEM jobs?",{"A":20,"Q":21},"Apply or explore related openings through Rex.zone, using the job titles and metadata on this page to match the right role and workflow focus.","Where should applicants apply?","highest-paying-stem-jobs-united-states",{"desc":24,"title":25,"content":26},"Explore highest paying STEM jobs in the United States with Rex.zone, featuring remote, full-time roles aligned to real-world AI\u002FML training workflows. These jobs power LLM training pipelines through RLHF, data labeling, QA evaluation, prompt evaluation, content safety labeling, NLP named entity recognition, and computer vision annotation. If you are a Mid-Senior engineer looking for a Remote US role, review the job details below, verify LinkedIn-ready metadata, and apply through Rex.zone for high-impact work that improves training data quality, annotation guidelines compliance, and model performance improvement across modern AI systems.","Highest Paying STEM Jobs in the United States",[27,30,33,36,39],{"h2":28,"desc":29},"Highest Paying STEM Job: Remote AI\u002FML Data Annotation Engineer","LinkedIn Job Metadata: Title: Remote AI\u002FML Data Annotation 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: RLHF, data labeling, LLM evaluation, prompt evaluation, training data quality, QA evaluation, annotation guidelines compliance, named entity recognition, computer vision annotation, content safety labeling, model performance improvement; Salary Currency: USD; Salary Min: 63360; Salary Max: 126720; Pay Period: YEAR. Role Overview: Build and maintain scalable annotation workflows for AI\u002FML, focusing on high-quality training data creation and evaluation. You will design labeling schemas, write annotation guidelines, and run QA evaluation loops that improve large language model evaluation outcomes. Responsibilities: Own training data quality metrics and error taxonomies; implement review queues and inter-annotator agreement checks; perform RLHF preference labeling and ranking; run prompt evaluation and regression test sets; support NLP tasks like named entity recognition and intent classification; support computer vision annotation (bounding boxes, segmentation) when needed; execute content safety labeling for policy-aligned model behavior; partner with engineering to integrate tooling into LLM training pipelines. Qualifications: 3+ years in data operations, ML ops, or annotation engineering; strong understanding of NLP, LLM evaluation, and human feedback signals; experience with QA evaluation programs and sampling strategies; familiarity with annotation tools and workflow automation; excellent written guidelines and ambiguity resolution skills.",{"h2":31,"desc":32},"Highest Paying STEM Job: Remote RLHF Evaluation Engineer (LLM)","LinkedIn Job Metadata: Title: Remote RLHF Evaluation Engineer (LLM); 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: RLHF, preference ranking, LLM evaluation, prompt evaluation, QA evaluation, training data quality, model performance improvement, rubric design, annotation guidelines compliance, safety evaluation, adversarial testing, red teaming; Salary Currency: USD; Salary Min: 63360; Salary Max: 126720; Pay Period: YEAR. Role Overview: Drive RLHF and evaluation programs that increase model alignment, helpfulness, and robustness. You will define rubrics, build evaluation datasets, and run QA evaluation cycles to quantify model performance improvement. Responsibilities: Design preference ranking tasks and rubrics; calibrate evaluators and resolve edge cases; build prompt evaluation suites and scorecards; conduct safety evaluation for content policy compliance; create adversarial tests and red-team-style evaluations; analyze disagreement and bias signals; communicate evaluation findings to LLM training pipeline stakeholders; enforce annotation guidelines compliance across projects. Qualifications: 3+ years in evaluation, QA, data labeling, or applied NLP; strong understanding of RLHF concepts and human feedback data; experience with rubric design, sampling, and measurement; ability to translate ambiguous quality goals into measurable criteria; strong technical writing for guidelines and evaluation protocols.",{"h2":34,"desc":35},"Highest Paying STEM Job: Remote NLP Data Labeling Engineer (Named Entity Recognition)","LinkedIn Job Metadata: Title: Remote NLP Data Labeling Engineer (Named Entity Recognition); 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, NLP annotation, named entity recognition, entity linking, taxonomy design, annotation guidelines compliance, training data quality, QA evaluation, LLM training pipelines, prompt evaluation, ambiguity resolution, dataset auditing; Salary Currency: USD; Salary Min: 63360; Salary Max: 126720; Pay Period: YEAR. Role Overview: Engineer high-quality labeled NLP datasets for production-grade models, with emphasis on named entity recognition, entity linking, and consistent taxonomy design. Your work directly impacts training data quality and downstream model performance improvement in LLM training pipelines. Responsibilities: Define entity schemas and taxonomies; write and iterate annotation guidelines; run dataset auditing and QA evaluation sampling; manage edge-case adjudication and guideline updates; create gold sets and calibration tasks; support prompt evaluation using labeled test suites; collaborate with engineering on ingestion, versioning, and dataset documentation. Qualifications: 3+ years in NLP annotation or data operations; hands-on NER labeling and guideline ownership; experience with QA evaluation methods and disagreement analysis; strong attention to detail and reproducible dataset practices.",{"h2":37,"desc":38},"Highest Paying STEM Job: Remote Computer Vision Annotation Engineer","LinkedIn Job Metadata: Title: Remote Computer Vision Annotation 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: computer vision annotation, bounding boxes, segmentation, keypoints, data labeling, training data quality, QA evaluation, annotation guidelines compliance, dataset curation, active learning, error analysis, model performance improvement; Salary Currency: USD; Salary Min: 63360; Salary Max: 126720; Pay Period: YEAR. Role Overview: Deliver high-fidelity computer vision annotation for model training and evaluation, including bounding boxes, segmentation, and keypoints. You will own annotation guidelines compliance and QA evaluation processes that lift model performance improvement in vision pipelines. Responsibilities: Build labeling specs for multi-class datasets; implement QA evaluation checks (spot checks, gold tasks, consensus review); curate datasets and handle long-tail edge cases; support active learning loops and prioritize high-value samples; produce error analysis reports tied to training data quality; coordinate with toolchain engineering for workflow efficiency. Qualifications: 3+ years in CV data labeling or dataset operations; strong grasp of annotation quality drivers and QA evaluation design; experience with segmentation and geometry edge cases; ability to write precise, testable annotation guidelines.",{"h2":40,"desc":41},"Highest Paying STEM Job: Remote Content Safety Labeling Engineer (AI Policy QA)","LinkedIn Job Metadata: Title: Remote Content Safety Labeling Engineer (AI Policy QA); 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: content safety labeling, safety evaluation, policy taxonomy, QA evaluation, RLHF, prompt evaluation, annotation guidelines compliance, risk assessment, harm detection, red teaming, dataset auditing, training data quality; Salary Currency: USD; Salary Min: 63360; Salary Max: 126720; Pay Period: YEAR. Role Overview: Build and validate content safety datasets that guide policy-aligned model behavior. You will execute safety evaluation, content safety labeling, and QA evaluation workflows that improve LLM training pipelines and reduce harmful outputs. Responsibilities: Develop safety taxonomies and decision rules; label and review sensitive content according to policy; run QA evaluation programs with calibrated reviewers; build prompt evaluation sets focused on jailbreaks and edge cases; support RLHF datasets for refusal and safe completion behavior; audit datasets for consistency and drift; report on training data quality and policy coverage gaps. Qualifications: 3+ years in content moderation, safety QA, data labeling, or evaluation; strong judgment and consistency under detailed policy; experience translating policy into annotation guidelines compliance; familiarity with LLM safety evaluation methods and adversarial prompting.","STEM Careers"]