Remote STEM Jobs In Canada

Remote STEM Jobs in Canada (Full Time) Rex.zone connects mid-senior engineers and STEM professionals to real-world AI/ML training workflows, including LLM evaluation, RLHF-style preference ranking, data labeling, QA evaluation, and prompt evaluation. You will help improve model performance by producing and reviewing high-quality training data and enforcing annotation guidelines compliance. What You Will Do - Contribute to training data quality through labeling, review, and adjudication - Perform RLHF-style preference ranking and helpfulness/harmlessness evaluations - Execute prompt evaluation and response grading for large language model evaluation - Apply annotation guidelines, document edge cases, and support rubric adherence - Run QA evaluation workflows, track defects, and recommend process improvements - Support NLP tasks (e.g., named entity recognition, taxonomy tagging) - Support computer vision annotation (e.g., bounding boxes, polygons, classification) - Support content safety labeling (policy categories, risk scoring, refusals) - Collaborate with teams across AI labs, tech startups, annotation vendors, and BPO operations Required Qualifications - Mid-senior experience in STEM or engineering - Strong analytical writing and attention to detail for evaluation rubrics - Familiarity with AI/ML concepts, LLM behavior, and model failure modes - Experience with data labeling, QA evaluation, or guideline-driven review - Ability to work full-time remotely with reliable internet and secure work practices Preferred Qualifications - Exposure to RLHF, prompt evaluation, and rubric-based grading - Experience with NLP and/or computer vision annotation - Experience with content safety labeling and policy enforcement - Comfort using annotation platforms, spreadsheets, and issue trackers - Ability to mentor peers on annotation guidelines compliance and training data quality How To Apply Apply via Rex.zone and highlight your STEM/engineering background, guideline-driven work, and examples that improved training data quality or model performance.