[{"data":1,"prerenderedAt":55},["ShallowReactive",2],{"job-ai-jobs-in-canada":3},{"Ques":4,"Slug":28,"Header":29,"job_category":54},{"title":5,"content":6},"Frequently Asked Questions",[7,10,13,16,19,22,25],{"A":8,"Q":9},"These roles are Remote and Full-Time, designed to support distributed AI\u002FML data operations through Rex.zone.","Are these AI jobs in Canada remote or on-site?",{"A":11,"Q":12},"Common workflows include RLHF, data labeling, QA evaluation, prompt evaluation, named entity recognition, computer vision annotation, and content safety labeling tied to LLM training pipelines.","What kind of AI work is included in AI jobs in Canada?",{"A":14,"Q":15},"You compare candidate model outputs, rank preferences, provide rubric-based rationales, and help create high-signal feedback that improves alignment and model performance improvement.","What does RLHF evaluation involve day to day?",{"A":17,"Q":18},"You will typically use web-based annotation platforms, task queues, calibration rubrics, audit workflows, and documentation tools to ensure annotation guidelines compliance and training data quality.","What tools will I use for data labeling and QA evaluation?",{"A":20,"Q":21},"This posting is for FULL_TIME at Mid-Senior level; Rex.zone may also list remote, contract, freelance, entry-level, and senior roles depending on current employer needs.","Do you offer contract, freelance, entry-level, or senior options?",{"A":23,"Q":24},"The role can include NLP and LLM evaluation as core, with optional computer vision annotation and content safety labeling depending on the project.","Which domains are covered: NLP, computer vision, content safety, or LLM training?",{"A":26,"Q":27},"Projects may come from AI labs, tech startups, enterprise teams, BPOs, and annotation vendors that need dependable training data quality and large language model evaluation support.","What types of employers use Rex.zone for these roles?","ai-jobs-in-canada",{"desc":30,"title":31,"content":32},"AI jobs in Canada on Rex.zone focus on search-recognizable AI\u002FML workstreams that power real training pipelines: data labeling, RLHF, prompt evaluation, QA evaluation, and content safety labeling for large language models. These remote, full-time roles support model performance improvement through training data quality, annotation guidelines compliance, and rigorous evaluation workflows across NLP and computer vision. Explore AI jobs in Canada with clear expectations, measurable quality targets, and production-grade tooling used by AI labs, tech startups, BPOs, and annotation vendors. Apply to build reliable datasets, reduce model risk, and improve LLM alignment through systematic evaluation and feedback.","AI Jobs in Canada",[33,36,39,42,45,48,51],{"h2":34,"desc":35},"AI Jobs in Canada (Remote, Full-Time) — Open Role","Title: AI Jobs in 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: AI data annotation, RLHF evaluation, data labeling, prompt evaluation, QA evaluation, named entity recognition, LLM training pipelines, content safety labeling\nSalary Currency: USD\nSalary Min: 63360\nSalary Max: 126720\nPay Period: YEAR",{"h2":37,"desc":38},"About the Role","You will support AI jobs in Canada by operating end-to-end data operations that improve large language model evaluation and training data quality. Work includes RLHF tasks, data labeling, prompt evaluation, and QA evaluation using detailed rubrics to drive model performance improvement. You will apply annotation guidelines compliance, perform spot checks, document edge cases, and collaborate with cross-functional teams to stabilize quality metrics across NLP and computer vision annotation workflows. This role is Remote and Full-Time via Rex.zone and supports multiple employer types including AI labs, tech startups, BPOs, and annotation vendors.",{"h2":40,"desc":41},"What You Will Do","You will execute RLHF evaluation and preference ranking, perform prompt evaluation and response grading for helpfulness\u002Fharmlessness\u002Fhonesty, complete data labeling tasks including named entity recognition and text classification, support computer vision annotation such as bounding boxes and segmentation when needed, run QA evaluation through audits and inter-annotator agreement checks, apply content safety labeling policies, maintain annotation guidelines compliance with detailed documentation, and surface systematic failure modes that impact LLM training pipelines and evaluation sets.",{"h2":43,"desc":44},"Required Qualifications","Mid-Senior experience in AI data operations or applied ML support, strong written reasoning for rubric-based evaluation, familiarity with RLHF concepts and large language model evaluation, experience with data labeling taxonomies (classification, NER, summarization evaluation), ability to follow and refine annotation guidelines compliance, comfort using web-based labeling tools and spreadsheets, and an engineering-oriented approach to quality measurement, sampling, and continuous improvement.",{"h2":46,"desc":47},"Preferred Qualifications","Experience with prompt evaluation frameworks, content safety labeling, policy-driven QA evaluation, computer vision annotation workflows, dataset versioning and issue tracking, calibration sessions for inter-annotator agreement, and understanding of how training data quality affects model performance improvement across NLP, CV, and multimodal systems.",{"h2":49,"desc":50},"Quality and Performance Expectations","You will be evaluated on training data quality, annotation guidelines compliance, audit pass rates, calibration consistency, turnaround time, and the ability to document edge cases that affect large language model evaluation. The team values clear rationales, repeatable decision-making, and proactive identification of label noise that can degrade LLM training pipelines.",{"h2":52,"desc":53},"How to Apply on Rex.zone","Apply through Rex.zone and include a brief summary of your RLHF evaluation, data labeling, prompt evaluation, or QA evaluation experience. If available, share examples of how you improved training data quality, reduced ambiguity in annotation guidelines compliance, or supported model performance improvement for large language model evaluation.","AI Data Operations",1786024776786]