[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"job-stem-degree-jobs-canada":3},{"Ques":4,"Slug":25,"Header":26,"job_category":54},{"title":5,"content":6},"Frequently Asked Questions",[7,10,13,16,19,22],{"A":8,"Q":9},"It refers to remote full-time engineering roles suited to STEM graduates, often supporting software engineering, data engineering, and AI\u002FML workflows such as data labeling, RLHF evaluation, prompt evaluation, and QA evaluation for LLM training pipelines.","What does “STEM Degree Jobs in Canada” mean on Rex.zone?",{"A":11,"Q":12},"Yes. The Remote Type is Remote, and the role is designed for distributed collaboration with documented specs, rubrics, and review workflows.","Is this role remote?",{"A":14,"Q":15},"Common tasks include training data quality improvements, annotation guidelines compliance, named entity recognition, computer vision annotation, content safety labeling, RLHF preference ranking, and large language model evaluation via prompt evaluation and QA audits.","What kind of AI\u002FML tasks are included?",{"A":17,"Q":18},"It is helpful but not always required. Mid-senior candidates should demonstrate strong engineering fundamentals, quality-minded execution, and the ability to learn rubrics and evaluation protocols quickly.","Do I need prior RLHF or data labeling experience?",{"A":20,"Q":21},"Rex.zone lists remote, full-time roles and may also feature contract, freelance, entry-level, and senior pathways depending on project demand across NLP, computer vision, content safety, and LLM training.","What job modifiers does Rex.zone support for these roles?",{"A":23,"Q":24},"Quality is measured through QA evaluation, audit sampling, calibration agreement, error taxonomy tracking, and adherence to annotation guidelines compliance, with downstream feedback from model performance improvement and large language model evaluation results.","How is quality measured?","stem degree jobs canada",{"desc":27,"title":28,"content":29},"STEM Degree Jobs in Canada are full-time remote engineering roles where STEM graduates apply software engineering, data engineering, and applied AI\u002FML skills to build and evaluate production systems. At Rex.zone (Rexzone), you will contribute to real-world AI training workflows such as data labeling, RLHF evaluation, prompt evaluation, named entity recognition, computer vision annotation, and content safety labeling—improving training data quality, annotation guidelines compliance, and large language model evaluation outcomes for model performance improvement. Explore remote, contract, freelance, entry-level, and senior pathways across NLP, computer vision, and LLM training pipelines while staying aligned with measurable QA evaluation standards and delivery SLAs.","STEM Degree Jobs in Canada",[30,33,36,39,42,45,48,51],{"h2":31,"desc":32},"STEM Degree Jobs in Canada — LinkedIn Job Metadata","Title: STEM Degree Jobs in Canada | 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, Data Engineering, Applied Machine Learning, NLP, Computer Vision, Data Labeling, RLHF, Prompt Evaluation, QA Evaluation, Named Entity Recognition, Content Safety, LLM Training Pipelines | Salary Currency: USD | Salary Min: 63360 | Salary Max: 126720 | Pay Period: YEAR",{"h2":34,"desc":35},"About the Role","You will join Rex.zone to support end-to-end engineering and AI\u002FML workflows that convert raw data into reliable training datasets and evaluation signals. The work includes building internal tooling, improving annotation guidelines compliance, running QA evaluation and audits, and partnering with model and product teams to drive model performance improvement. Depending on the project, you may contribute to NLP tasks (named entity recognition, text classification), LLM evaluation (prompt evaluation, RLHF preference ranking), computer vision annotation (bounding boxes, segmentation), and content safety labeling aligned to policy and risk requirements.",{"h2":37,"desc":38},"Key Responsibilities","Deliver high-quality outputs for training data quality and evaluation integrity across multiple modalities and domains. Typical responsibilities include: designing workflow checks for QA evaluation; implementing or improving data pipelines for labeled datasets; performing RLHF preference labeling and rubric-based prompt evaluation; validating annotation guidelines compliance through sampling, adjudication, and reviewer feedback; supporting NLP labeling such as named entity recognition and intent classification; supporting computer vision annotation such as boxes, polygons, and keypoints; executing content safety labeling with consistent taxonomy and escalation; documenting decisions, edge cases, and dataset versions for reproducible LLM training pipelines.",{"h2":40,"desc":41},"Required Qualifications","STEM degree or equivalent practical experience in engineering, computer science, data science, mathematics, statistics, or related fields. Mid-senior experience delivering production-quality work in software engineering or data-centric workflows. Ability to follow and improve detailed rubrics, maintain annotation guidelines compliance, and operate with measurable quality thresholds. Familiarity with AI\u002FML concepts, dataset versioning, and evaluation methods used in large language model evaluation and model performance improvement.",{"h2":43,"desc":44},"Preferred Qualifications","Experience with AI data operations, annotation vendors, BPO-style delivery models, or internal tooling for labeling and review. Exposure to RLHF workflows, prompt evaluation methodologies, and safety or policy-driven content safety labeling. Background in NLP (named entity recognition, classification) and\u002For computer vision annotation (segmentation, detection). Comfort working cross-functionally with AI labs, tech startups, and platform teams to define acceptance criteria for training data quality and QA evaluation.",{"h2":46,"desc":47},"Tools, Workflows, and Quality Standards","You will work in structured queues with audit trails, clear rubrics, and quality gates. Work typically includes reviewer calibration, adjudication, dataset sampling, error taxonomies, and continuous process improvement. Success is measured by annotation guidelines compliance, training data quality metrics, throughput aligned to SLAs, and downstream signals from large language model evaluation, including reduced disagreement rates and improved model performance improvement on targeted benchmarks.",{"h2":49,"desc":50},"Remote Work and Collaboration","This is a Remote, FULL_TIME role with written-first collaboration. You will coordinate with distributed stakeholders through specs, tickets, and review feedback loops. Projects may be organized by domain (NLP, computer vision, content safety) or by workflow stage (data labeling, QA evaluation, RLHF, prompt evaluation) depending on customer and model needs.",{"h2":52,"desc":53},"How to Apply","Apply through Rex.zone to be matched with remote STEM degree job opportunities in Canada-aligned pipelines and global engineering programs. Include a resume highlighting engineering delivery, data-centric work, QA practices, and any AI\u002FML evaluation or labeling experience (RLHF, prompt evaluation, named entity recognition, computer vision annotation, content safety labeling).","Engineering"]