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Remote STEM Jobs in Canada

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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 ApplyApply via Rex.zone and highlight your STEM/engineering background, guideline-driven work, and examples that improved training data quality or model performance.

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