[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"job-online-stem-jobs-canada":3},{"Ques":4,"Slug":31,"Header":32,"job_category":63},{"title":5,"content":6},"Frequently Asked Questions",[7,10,13,16,19,22,25,28],{"A":8,"Q":9},"Yes. Remote Type is Remote and the work is performed fully remotely.","Is this role remote?",{"A":11,"Q":12},"Many modern engineering teams support AI\u002FML training pipelines where software, tooling, and evaluation design determine training data quality. RLHF, data labeling, and QA evaluation are core inputs to large language model evaluation and model performance improvement.","Why does an online STEM role include data labeling and RLHF evaluation?",{"A":14,"Q":15},"Common projects include building evaluation harnesses, improving annotation tooling, running prompt evaluation, establishing annotation guidelines compliance programs, and supporting NLP and computer vision annotation quality systems for LLM training pipelines.","What kinds of projects are common for these online STEM jobs in Canada?",{"A":17,"Q":18},"Yes. Employment Type is FULL_TIME.","Is this position full-time?",{"A":20,"Q":21},"Experience Level is Mid-Senior. You should be able to work independently, define metrics, and deliver production-ready improvements to evaluation and data workflows.","What experience level is expected?",{"A":23,"Q":24},"Emphasize Python, machine learning fundamentals, RLHF, data labeling, QA evaluation, prompt evaluation, NLP (including named entity recognition), computer vision annotation, content safety labeling, and experience improving training data quality.","What skills should I emphasize in my application?",{"A":26,"Q":27},"Salary Currency is USD, Salary Min is 63360, Salary Max is 126720, and Pay Period is YEAR.","What is the salary range and pay period?",{"A":29,"Q":30},"Yes. While this posting is full-time and Mid-Senior, Rex.zone also supports remote contract, freelance, entry-level, and senior opportunities depending on project needs.","Does the page cover other job modifiers like contract, freelance, entry-level, or senior?","online-stem-jobs-canada",{"desc":33,"title":34,"content":35},"Online STEM jobs in Canada at Rex.zone focus on remote engineering work that supports AI\u002FML training pipelines, including RLHF evaluation, data labeling, QA evaluation, prompt evaluation, and model performance improvement. You will collaborate with distributed teams to build and validate NLP and computer vision workflows, apply annotation guidelines compliance, and improve training data quality for large language model evaluation. This role is full-time and remote, designed for candidates who want Canadian online STEM work while contributing to production-grade technology systems and measurable quality metrics across labeling, evaluation, and content safety labeling.","Online STEM Jobs Canada",[36,39,42,45,48,51,54,57,60],{"h2":37,"desc":38},"Online STEM Jobs Canada — Remote Engineering","Title: Online STEM Engineer (Canada)\nDate: 25-02-2026\nCompany: Rexzone\nCountry: US\nRemote Type: Remote\nEmployment Type: FULL_TIME\nExperience Level: Mid-Senior\nIndustry: Technology\nJob Function: Engineering\nSkills: STEM engineering, Python, machine learning, data labeling, RLHF, QA evaluation, prompt evaluation, NLP, computer vision annotation, named entity recognition, LLM evaluation, training data quality\nSalary Currency: USD\nSalary Min: 63360\nSalary Max: 126720\nPay Period: YEAR",{"h2":40,"desc":41},"About Rex.zone","Rex.zone connects remote STEM talent with AI labs, tech startups, and enterprise teams that need high-quality engineering and evaluation workflows. Our projects span LLM training pipelines, data operations, and production systems where training data quality, annotation guidelines compliance, and measurable QA evaluation directly impact model performance improvement.",{"h2":43,"desc":44},"What You Will Do","Own end-to-end remote engineering tasks supporting AI\u002FML data and evaluation workflows, including RLHF scoring and rubric design. Build and maintain annotation tools or lightweight pipelines to improve training data quality and throughput. Execute QA evaluation and audit sampling to ensure annotation guidelines compliance across NLP and computer vision annotation tasks. Run prompt evaluation and large language model evaluation to detect regressions, bias, and failure modes. Partner with stakeholders to define metrics, acceptance criteria, and documentation for model performance improvement.",{"h2":46,"desc":47},"Core Workstreams (NLP, CV, Content Safety)","NLP: named entity recognition, text classification, instruction following evaluation, response ranking, and prompt evaluation. Computer vision: bounding boxes, polygons, keypoints, and computer vision annotation QA with inter-annotator agreement checks. Content safety labeling: policy-aligned labeling, edge-case taxonomy building, and escalation workflows for sensitive content.",{"h2":49,"desc":50},"Required Qualifications","Mid-Senior experience delivering engineering work in production or applied research environments. Strong Python skills and comfort with data processing, evaluation harnesses, and reproducible experiments. Familiarity with machine learning concepts, LLM evaluation, and the practical realities of data labeling at scale. Experience creating or enforcing annotation guidelines compliance and performing QA evaluation. Ability to communicate clearly in distributed, remote-first teams.",{"h2":52,"desc":53},"Preferred Qualifications","Hands-on RLHF workflows (ranking, rubric-based grading, preference data generation) and evaluator calibration techniques. Experience with NLP tasks such as named entity recognition, semantic similarity, and retrieval evaluation. Experience with computer vision annotation tooling and QA sampling methodologies. Background in content safety labeling or trust and safety operations. Exposure to measurement design: precision\u002Frecall, agreement metrics, and error taxonomy frameworks.",{"h2":55,"desc":56},"How Success Is Measured","Improved training data quality and reduced defect rates through consistent QA evaluation and audit practices. Higher annotation guidelines compliance and clearer labeling documentation. Faster evaluation cycles for large language model evaluation and prompt evaluation. Observable model performance improvement tied to better preference data, cleaner labels, and reliable metrics.",{"h2":58,"desc":59},"Work Arrangement","Remote: This role remains fully remote. Full-time: You will be scheduled on a full-time basis. We also partner with teams that hire contract or freelance talent and can discuss fit if project requirements change.",{"h2":61,"desc":62},"Apply on Rex.zone","Explore online STEM jobs in Canada on Rex.zone and submit your application with a resume and short summary of relevant NLP, computer vision annotation, RLHF, data labeling, or QA evaluation experience. Include examples of documentation you have written (rubrics, guidelines, evaluation plans) and any reproducible work samples where applicable.","Engineering"]