About the role
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.
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