STEM Careers Canada

This Remote Full-Time STEM Careers Canada role at Rex.zone (Rexzone) supports AI/ML training workflows through data labeling, RLHF evaluation, prompt evaluation, and QA checks that improve training data quality and model performance improvement for large language models. You will apply annotation guidelines compliance, content safety labeling, and structured evaluation methods across NLP, named entity recognition, and computer vision annotation tasks, helping AI labs, tech startups, BPOs, and annotation vendors deliver reliable LLM training pipelines. Explore and apply on Rex.zone to join a distributed engineering team focused on scalable, measurable evaluation and high-quality labeled data.

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LinkedIn Job Metadata

Keyword: STEM Careers Canada | Job Title: STEM Careers Canada | Date Posted: 25-02-2026 | Company: Rexzone | Country: US | Workplace Type: Remote | Employment Type: FULL_TIME | Experience Level: Mid-Senior | Industry: Technology | Job Function: Engineering | Skills: data labeling, RLHF, prompt evaluation, QA evaluation, training data quality, annotation guidelines compliance, LLM evaluation, named entity recognition, computer vision annotation, content safety labeling, LLM training pipelines | Salary Currency: USD | Salary Min: 63360 | Salary Max: 126720 | Pay Period: YEAR

About the Role

You will work remotely on Rex.zone to support STEM careers pathways by contributing to real-world AI/ML production pipelines. Your day-to-day work includes data labeling, RLHF-style preference ranking, prompt evaluation, and QA evaluation that directly improves training data quality and model performance improvement. You will follow annotation guidelines compliance, document edge cases, and collaborate with engineering stakeholders to refine labeling rubrics for NLP, named entity recognition, computer vision annotation, and content safety labeling tasks.

Key Responsibilities

Responsibilities include: (1) Perform high-accuracy data labeling for text, image, and multimodal datasets, (2) Execute RLHF evaluation tasks such as preference comparisons and rubric-based scoring, (3) Run prompt evaluation and response grading for large language model evaluation, (4) Apply content safety labeling and policy-aware categorization, (5) Maintain annotation guidelines compliance and provide feedback to improve guidelines, (6) Conduct QA evaluation, audits, and disagreement resolution to ensure training data quality, (7) Track metrics that connect labeling quality to model performance improvement, (8) Support dataset versioning notes and clear handoffs across distributed teams.

Required Qualifications

Qualifications include: (1) Mid-Senior experience in engineering-adjacent data operations, evaluation, or annotation programs, (2) Demonstrated ability to follow detailed rubrics with high consistency and low error rates, (3) Familiarity with LLM training pipelines, LLM evaluation concepts, and preference-based grading (RLHF), (4) Working knowledge of NLP concepts such as named entity recognition and taxonomy design, (5) Exposure to computer vision annotation workflows (bounding boxes, segmentation, classification) is preferred, (6) Strong written communication for edge-case documentation, (7) Comfort working in Remote, full-time environments with asynchronous collaboration.

Skills and Tools

Core skills for this role include data labeling, RLHF, prompt evaluation, QA evaluation, training data quality, annotation guidelines compliance, LLM evaluation, named entity recognition, computer vision annotation, content safety labeling, and LLM training pipelines. You should be comfortable learning new annotation tools, performing structured reviews, and applying consistent decision-making under clear rubrics.

Salary and Employment Details

This is a Remote FULL_TIME role based in the US market. Compensation range is USD 63360 to 126720 per YEAR, depending on experience, role scope, and evaluation performance. Rexzone provides standardized processes for quality, privacy, and secure handling of sensitive datasets where applicable.

How to Apply on Rex.zone

Apply through Rex.zone by submitting your profile and highlighting experience with data labeling, QA evaluation, prompt evaluation, RLHF, and any NLP, named entity recognition, or computer vision annotation work. Include examples of rubric-based decisioning, quality metrics, and documentation you have produced to improve training data quality and model performance improvement.

Frequently Asked Questions

  • Q: Is this role remote and full-time?

    Yes. The role is explicitly Remote and FULL_TIME, with asynchronous collaboration as the default working style.

  • Q: What does “STEM Careers Canada” mean in this job context?

    It is the keyword-aligned job entity for this posting and focuses on STEM career-aligned work that supports AI/ML engineering workflows on Rex.zone, including data labeling and evaluation tasks.

  • Q: What types of tasks will I do day to day?

    You will perform data labeling, RLHF-style preference ranking, prompt evaluation, QA evaluation, and guideline-based reviews to improve training data quality for LLM training pipelines.

  • Q: Which domains are covered (NLP, CV, content safety)?

    The work may include NLP tasks such as named entity recognition, computer vision annotation tasks, and content safety labeling depending on project needs.

  • Q: What skills are most important for success?

    High-precision rubric application, annotation guidelines compliance, strong writing for edge cases, and experience with QA evaluation and large language model evaluation are key.

  • Q: What is the salary range and pay period?

    The salary range is USD 63360 to 126720 per YEAR.

  • Q: What kinds of employers use this kind of work?

    Projects commonly support AI labs, tech startups, BPOs, and annotation vendors that run LLM training pipelines and need consistent evaluation and labeling.

  • Q: How do I apply?

    Apply via Rex.zone with your relevant experience in data labeling, RLHF evaluation, prompt evaluation, QA evaluation, and any NLP or computer vision annotation background.

230+Domains Covered
120K+PhD, Specialist, Experts Onboarded
50+Countries Represented

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