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
ā¢ Contribute to training data quality through labeling, review, and adjudication
ā¢ Perform RLHF-style preference ranking and helpfulness/harmlessness evaluations
ā¢ Execute prompt evaluation and response grading for large language model evaluation
ā¢ Apply annotation guidelines, document edge cases, and support rubric adherence
ā¢ Run QA evaluation workflows, track defects, and recommend process improvements
ā¢ Support NLP tasks (e.g., named entity recognition, taxonomy tagging)
ā¢ Support computer vision annotation (e.g., bounding boxes, polygons, classification)
ā¢ Support content safety labeling (policy categories, risk scoring, refusals)
ā¢ Collaborate with teams across AI labs, tech startups, annotation vendors, and BPO operations
Required Qualifications
ā¢ Mid-senior experience in STEM or engineering
ā¢ Strong analytical writing and attention to detail for evaluation rubrics
ā¢ Familiarity with AI/ML concepts, LLM behavior, and model failure modes
ā¢ Experience with data labeling, QA evaluation, or guideline-driven review
ā¢ Ability to work full-time remotely with reliable internet and secure work practices
Preferred Qualifications
ā¢ Exposure to RLHF, prompt evaluation, and rubric-based grading
ā¢ Experience with NLP and/or computer vision annotation
ā¢ Experience with content safety labeling and policy enforcement
ā¢ Comfort using annotation platforms, spreadsheets, and issue trackers
ā¢ 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.
Apply Now
Apply Now
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
ā¢ Contribute to training data quality through labeling, review, and adjudication
ā¢ Perform RLHF-style preference ranking and helpfulness/harmlessness evaluations
ā¢ Execute prompt evaluation and response grading for large language model evaluation
ā¢ Apply annotation guidelines, document edge cases, and support rubric adherence
ā¢ Run QA evaluation workflows, track defects, and recommend process improvements
ā¢ Support NLP tasks (e.g., named entity recognition, taxonomy tagging)
ā¢ Support computer vision annotation (e.g., bounding boxes, polygons, classification)
ā¢ Support content safety labeling (policy categories, risk scoring, refusals)
ā¢ Collaborate with teams across AI labs, tech startups, annotation vendors, and BPO operations
Required Qualifications
ā¢ Mid-senior experience in STEM or engineering
ā¢ Strong analytical writing and attention to detail for evaluation rubrics
ā¢ Familiarity with AI/ML concepts, LLM behavior, and model failure modes
ā¢ Experience with data labeling, QA evaluation, or guideline-driven review
ā¢ Ability to work full-time remotely with reliable internet and secure work practices
Preferred Qualifications
ā¢ Exposure to RLHF, prompt evaluation, and rubric-based grading
ā¢ Experience with NLP and/or computer vision annotation
ā¢ Experience with content safety labeling and policy enforcement
ā¢ Comfort using annotation platforms, spreadsheets, and issue trackers
ā¢ 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.
Apply Now
Apply Now