STEM Degree Jobs in Canada

STEM Degree Jobs in Canada are full-time remote engineering roles where STEM graduates apply software engineering, data engineering, and applied AI/ML skills to build and evaluate production systems. At Rex.zone (Rexzone), you will contribute to real-world AI training workflows such as data labeling, RLHF evaluation, prompt evaluation, named entity recognition, computer vision annotation, and content safety labeling—improving training data quality, annotation guidelines compliance, and large language model evaluation outcomes for model performance improvement. Explore remote, contract, freelance, entry-level, and senior pathways across NLP, computer vision, and LLM training pipelines while staying aligned with measurable QA evaluation standards and delivery SLAs.

STEM Degree Jobs in Canada — LinkedIn Job Metadata

About the Role

You will join Rex.zone to support end-to-end engineering and AI/ML workflows that convert raw data into reliable training datasets and evaluation signals. The work includes building internal tooling, improving annotation guidelines compliance, running QA evaluation and audits, and partnering with model and product teams to drive model performance improvement. Depending on the project, you may contribute to NLP tasks (named entity recognition, text classification), LLM evaluation (prompt evaluation, RLHF preference ranking), computer vision annotation (bounding boxes, segmentation), and content safety labeling aligned to policy and risk requirements.

Key Responsibilities

Deliver high-quality outputs for training data quality and evaluation integrity across multiple modalities and domains. Typical responsibilities include: designing workflow checks for QA evaluation; implementing or improving data pipelines for labeled datasets; performing RLHF preference labeling and rubric-based prompt evaluation; validating annotation guidelines compliance through sampling, adjudication, and reviewer feedback; supporting NLP labeling such as named entity recognition and intent classification; supporting computer vision annotation such as boxes, polygons, and keypoints; executing content safety labeling with consistent taxonomy and escalation; documenting decisions, edge cases, and dataset versions for reproducible LLM training pipelines.

Required Qualifications

STEM degree or equivalent practical experience in engineering, computer science, data science, mathematics, statistics, or related fields. Mid-senior experience delivering production-quality work in software engineering or data-centric workflows. Ability to follow and improve detailed rubrics, maintain annotation guidelines compliance, and operate with measurable quality thresholds. Familiarity with AI/ML concepts, dataset versioning, and evaluation methods used in large language model evaluation and model performance improvement.

Preferred Qualifications

Experience with AI data operations, annotation vendors, BPO-style delivery models, or internal tooling for labeling and review. Exposure to RLHF workflows, prompt evaluation methodologies, and safety or policy-driven content safety labeling. Background in NLP (named entity recognition, classification) and/or computer vision annotation (segmentation, detection). Comfort working cross-functionally with AI labs, tech startups, and platform teams to define acceptance criteria for training data quality and QA evaluation.

Tools, Workflows, and Quality Standards

You will work in structured queues with audit trails, clear rubrics, and quality gates. Work typically includes reviewer calibration, adjudication, dataset sampling, error taxonomies, and continuous process improvement. Success is measured by annotation guidelines compliance, training data quality metrics, throughput aligned to SLAs, and downstream signals from large language model evaluation, including reduced disagreement rates and improved model performance improvement on targeted benchmarks.

Remote Work and Collaboration

This is a Remote, FULL_TIME role with written-first collaboration. You will coordinate with distributed stakeholders through specs, tickets, and review feedback loops. Projects may be organized by domain (NLP, computer vision, content safety) or by workflow stage (data labeling, QA evaluation, RLHF, prompt evaluation) depending on customer and model needs.

How to Apply

Apply through Rex.zone to be matched with remote STEM degree job opportunities in Canada-aligned pipelines and global engineering programs. Include a resume highlighting engineering delivery, data-centric work, QA practices, and any AI/ML evaluation or labeling experience (RLHF, prompt evaluation, named entity recognition, computer vision annotation, content safety labeling).

Frequently Asked Questions

Q: What does “STEM Degree Jobs in Canada” mean on Rex.zone?
It refers to remote full-time engineering roles suited to STEM graduates, often supporting software engineering, data engineering, and AI/ML workflows such as data labeling, RLHF evaluation, prompt evaluation, and QA evaluation for LLM training pipelines.
Q: Is this role remote?
Yes. The Remote Type is Remote, and the role is designed for distributed collaboration with documented specs, rubrics, and review workflows.
Q: What kind of AI/ML tasks are included?
Common tasks include training data quality improvements, annotation guidelines compliance, named entity recognition, computer vision annotation, content safety labeling, RLHF preference ranking, and large language model evaluation via prompt evaluation and QA audits.
Q: Do I need prior RLHF or data labeling experience?
It is helpful but not always required. Mid-senior candidates should demonstrate strong engineering fundamentals, quality-minded execution, and the ability to learn rubrics and evaluation protocols quickly.
Q: What job modifiers does Rex.zone support for these roles?
Rex.zone lists remote, full-time roles and may also feature contract, freelance, entry-level, and senior pathways depending on project demand across NLP, computer vision, content safety, and LLM training.
Q: How is quality measured?
Quality is measured through QA evaluation, audit sampling, calibration agreement, error taxonomy tracking, and adherence to annotation guidelines compliance, with downstream feedback from model performance improvement and large language model evaluation results.

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