[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"job-stem-jobs-in-united-states":3},{"Ques":4,"Slug":31,"Header":32,"job_category":57},{"title":5,"content":6},"Frequently Asked Questions",[7,10,13,16,19,22,25,28],{"A":8,"Q":9},"Yes. The role is explicitly Remote and open to candidates located in the US.","Are these STEM jobs in the United States remote?",{"A":11,"Q":12},"This posting is for FULL_TIME employment. Other modifiers like contract or freelance may exist on Rex.zone, but this role remains full-time.","Is this role full-time or contract\u002Ffreelance?",{"A":14,"Q":15},"You will support AI\u002FML engineering workflows such as data labeling, RLHF evaluation, QA evaluation, prompt evaluation, named entity recognition, computer vision annotation, and content safety labeling used in LLM training pipelines.","What kind of STEM work will I do?",{"A":17,"Q":18},"RLHF (Reinforcement Learning from Human Feedback) refers to evaluation tasks like preference ranking and rubric-based grading that help align model outputs and improve model performance.","What does RLHF mean in this job?",{"A":20,"Q":21},"Training data quality focus, annotation guidelines compliance, QA evaluation discipline, clear written reasoning, and comfort working across NLP, computer vision annotation, and content safety labeling workflows.","What skills are most important for success?",{"A":23,"Q":24},"The Experience Level is Mid-Senior, with demonstrated ability to execute complex evaluation and quality assurance tasks independently.","What experience level is expected?",{"A":26,"Q":27},"Industry is Technology, and the work may support AI labs, tech startups, BPOs, and annotation vendors through Rex.zone assignments.","What industries and employer types does this role support?",{"A":29,"Q":30},"Quality is measured through training data quality metrics, calibration and QA results, consistency with rubrics, audit-ready documentation, and downstream model performance improvement.","How is quality measured in this role?","stem-jobs-in-united-states",{"desc":33,"title":34,"content":35},"STEM jobs in the United States are full-time engineering roles that build and evaluate AI\u002FML systems across real-world production workflows. At Rex.zone, you will contribute to large language model training pipelines through data labeling, RLHF evaluation, QA evaluation, prompt evaluation, named entity recognition, computer vision annotation, and content safety labeling to improve training data quality, annotation guidelines compliance, and model performance improvement. This remote role supports AI labs, tech startups, and annotation vendors with scalable data operations and rigorous evaluation practices. Apply to work on measurable outcomes like dataset accuracy, rubric adherence, and responsible AI coverage while collaborating with distributed teams across the US.","STEM Jobs in the United States",[36,39,42,45,48,51,54],{"h2":37,"desc":38},"STEM Jobs in the United States — Remote (Full-Time)","Title: STEM Jobs in the United States | 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: STEM engineering, AI\u002FML, LLM evaluation, RLHF, data labeling, QA evaluation, prompt evaluation, named entity recognition, computer vision annotation, content safety labeling, training data quality | Salary Currency: USD | Salary Min: 63360 | Salary Max: 126720 | Pay Period: YEAR",{"h2":40,"desc":41},"About the Role","You will work remotely on US-based STEM engineering projects that support AI\u002FML model development and evaluation. Your core work blends engineering rigor with annotation and evaluation workflows: applying annotation guidelines, performing RLHF and preference ranking, running QA evaluation for consistency, and carrying out prompt evaluation to measure instruction-following and safety behaviors. You will also contribute to named entity recognition tasks, computer vision annotation, and content safety labeling used to train and validate large language models and multimodal systems. Success is measured through training data quality, auditability, and model performance improvement in production-aligned pipelines at Rex.zone.",{"h2":43,"desc":44},"Key Responsibilities","Deliver high-quality labeled datasets for NLP and computer vision, including NER spans, classifications, and structured extraction; Perform RLHF evaluations such as preference ranking, rubric-based grading, and pairwise comparisons; Execute QA evaluation workflows including sampling, disagreement resolution, inter-annotator agreement tracking, and guideline compliance checks; Conduct prompt evaluation across domains to assess helpfulness, factuality, reasoning, and instruction adherence; Apply content safety labeling for policy categories such as violence, hate, harassment, sexual content, self-harm, and regulated goods; Document edge cases, propose guideline updates, and maintain clear decision logs for audit readiness; Collaborate with engineering and data operations stakeholders to improve tooling, throughput, and quality metrics; Support multiple employer contexts (AI labs, tech startups, BPOs, annotation vendors) while maintaining consistent standards.",{"h2":46,"desc":47},"Required Qualifications","Mid-Senior experience in a STEM or engineering role with strong analytical and quality-first execution; Demonstrated ability to follow detailed rubrics and apply annotation guidelines compliance consistently; Familiarity with AI\u002FML concepts, LLM training pipelines, evaluation methodology, and data operations; Experience with at least one: NLP labeling, named entity recognition, prompt evaluation, RLHF, or computer vision annotation; Strong written reasoning for justifying labels and evaluation decisions; Ability to work independently in a remote environment and meet quality and turnaround targets.",{"h2":49,"desc":50},"Preferred Qualifications","Hands-on experience with QA programs such as gold sets, calibration sessions, or adjudication workflows; Exposure to content safety labeling and responsible AI policy frameworks; Familiarity with measurement concepts like precision\u002Frecall, confusion matrices, and inter-annotator agreement; Experience supporting multi-domain datasets (customer support, healthcare, finance, legal, education) while maintaining rubric alignment; Comfort working with modern annotation tools and structured data formats.",{"h2":52,"desc":53},"Compensation and Benefits","Salary range: USD 63360 to 126720 per year (depending on scope, complexity, and experience). Full-time remote role with structured goals tied to training data quality, evaluation accuracy, and model performance improvement. Benefits may vary by assignment and location within the United States.",{"h2":55,"desc":56},"How to Apply","Apply through Rex.zone with a resume that highlights STEM engineering experience, AI\u002FML evaluation or data labeling exposure, and examples of quality assurance work. Include brief details on domains you have evaluated (NLP, computer vision, content safety) and any rubric-based decision-making or auditing processes you have used.","Engineering"]