[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"job-high-paying-stem-jobs":3},{"Slug":4,"Header":5,"Ques":39,"job_category":63},"high paying stem jobs",{"desc":6,"title":7,"content":8},"High-paying STEM jobs on Rex.zone cover software engineering, data science, machine learning, MLOps, and AI\u002FLLM training roles such as data labeling specialists, RLHF raters, prompt evaluators, computer vision annotators, and content safety analysts. These roles directly support LLM training pipelines, training data quality, annotation guidelines compliance, model performance improvement, and large language model evaluation across NLP and vision workflows. This page helps candidates discover remote, contract, freelance, and full-time opportunities with AI labs, tech startups, BPOs, and annotation vendors. Apply to senior or entry-level tracks and accelerate AI systems through RLHF, QA evaluation, named entity recognition, computer vision annotation, and content safety labeling—exclusively via Rex.zone.","High-Paying STEM Jobs at Rex.zone",[9,12,15,18,21,24,27,30,33,36],{"h2":10,"desc":11},"About These Roles","We recruit for high-compensation STEM tracks that power modern AI: ML engineers, data scientists, MLOps engineers, research scientists, software engineers, data labeling experts, RLHF raters, prompt evaluators, NER taggers, computer vision annotators, content safety reviewers, and QA evaluation specialists. Work spans foundation model training, fine-tuning, inference optimization, evaluation, and human-in-the-loop feedback.",{"h2":13,"desc":14},"Hiring Tracks","Openings include: Machine Learning Engineer (NLP\u002FCV), Data Scientist (LLM Analytics), MLOps Engineer (Training Pipelines), Software Engineer (Backend\u002FInfra), RLHF Rater, Prompt Evaluator, Data Labeling Specialist, QA Evaluation Analyst, NER Annotator, Computer Vision Annotator (bounding boxes, segmentation), Content Safety Analyst, and Evaluation Engineer for model performance benchmarking.",{"h2":16,"desc":17},"AI\u002FML Workflows & Impact","Contribute to LLM training pipelines: dataset creation, data labeling, annotation guidelines compliance, QA evaluation, prompt evaluation, RLHF pairwise preference rating, named entity recognition, computer vision annotation, content safety labeling, adversarial red-teaming, and large language model evaluation. Your work improves training data quality, reduces noise, and drives measurable model performance improvement.",{"h2":19,"desc":20},"Required Skills","Strong STEM foundation in algorithms, statistics, and programming (Python, C++ or Java). Familiarity with NLP, computer vision, and LLM evaluation. Experience with labeling tools, guidelines authoring, inter-annotator agreement, quality control, and data QA. For engineering tracks: PyTorch\u002FTensorFlow, distributed training, data pipelines, evaluation frameworks, MLOps (Docker, Kubernetes, Airflow), and monitoring. Clear communication and process discipline.",{"h2":22,"desc":23},"Compensation","We offer top-of-market pay aligned with role, geography, and seniority. Indicative ranges: engineering and research tracks are highly competitive with equity at AI labs and tech startups; specialized annotation and evaluation roles pay premium hourly or project rates; senior leaders may access bonus structures tied to model performance improvement and delivery milestones.",{"h2":25,"desc":26},"Work Models & Modifiers","Roles available as remote, contract, freelance, full-time, entry-level, and senior. Project-based and ongoing engagements are offered. Flexible schedules for evaluation and labeling tracks. Onsite options exist for secure data environments. Transparent expectations and standardized process documents ensure consistent delivery.",{"h2":28,"desc":29},"Domains & Employers","Domain types: NLP, computer vision, content safety, LLM training, multimodal, speech, and retrieval. Employer types: AI labs, tech startups, BPOs, annotation vendors, platform teams, and enterprise ML groups. Work includes foundation model pretraining, fine-tuning, and post-deployment evaluation.",{"h2":31,"desc":32},"Tools & Tech","Common stacks: Python, PyTorch, TensorFlow, Hugging Face, Weights & Biases, Ray, Triton, ONNX, Docker, Kubernetes, Airflow, Spark. Labeling platforms: segmentation and bounding box tools, NER taggers, text classification, safety taxonomies, and preference rating systems used in RLHF workflows.",{"h2":34,"desc":35},"Why Rex.zone","Rex.zone centralizes discovery, screening, and placement for high-paying STEM roles. We align candidates to projects where their skills directly improve LLM training pipelines and evaluation outcomes. Expect vetted employers, clear scopes, quality gates, and fair compensation.",{"h2":37,"desc":38},"How to Apply","Create a profile on Rex.zone, highlight domain expertise (NLP, CV, content safety), list tools, prior labeling\u002Fevaluation projects, and measurable model improvements. Upload code samples or case studies, complete a short skills assessment, and select preferred modifiers (remote, contract, full-time).",{"title":40,"content":41},"Frequently Asked Questions",[42,45,48,51,54,57,60],{"A":43,"Q":44},"It’s a role that directly contributes to AI systems—engineering, research, MLOps, data labeling, RLHF, QA evaluation, and prompt evaluation—where compensation reflects impact on training data quality, evaluation rigor, and model performance improvement.","What is a high-paying STEM job in the AI\u002FML context?",{"A":46,"Q":47},"We continuously recruit ML engineers, data scientists, MLOps engineers, software engineers, RLHF raters, prompt evaluators, NER annotators, computer vision annotators, content safety analysts, and QA evaluation specialists across NLP, CV, and LLM training.","Which roles are open now?",{"A":49,"Q":50},"Yes. Many employers support remote, contract, freelance, and full-time arrangements. Secure data projects may require onsite or hybrid. Entry-level and senior tracks are both available.","Are remote and contract options available?",{"A":52,"Q":53},"RLHF roles perform pairwise preference ratings and qualitative feedback to align models with human values. Results are fed into LLM training pipelines to improve safety, usefulness, and evaluation scores.","How does RLHF fit into these jobs?",{"A":55,"Q":56},"Hands-on experience with NLP or CV datasets, strict annotation guidelines compliance, QA evaluation methods, and familiarity with large language model evaluation. For engineering: strong coding, scalable pipelines, and experience with ML frameworks and MLOps.","What skills make me competitive?",{"A":58,"Q":59},"AI labs, tech startups, BPOs, and annotation vendors seeking talent for data labeling, evaluation, MLOps, and engineering across LLM training and computer vision programs.","What employers hire through Rex.zone?",{"A":61,"Q":62},"Sign up on Rex.zone, complete skills and domain preferences, pass a short assessment, and our team matches you to roles that fit your expertise and compensation goals.","How do I apply and get matched?","STEM"]