AI Research Jobs in Canada (Remote, Full-Time)

AI Research jobs in Canada focus on building and evaluating machine learning systems used in real-world AI/ML training workflows, including LLM training pipelines, RLHF, prompt evaluation, and model performance improvement. On Rex.zone, you can explore Remote, FULL_TIME opportunities aligned with NLP, computer vision, and content safety labeling, plus research engineering responsibilities like experiment design, data labeling strategy, and QA evaluation. These roles support training data quality, annotation guidelines compliance, and scalable evaluation frameworks used by AI labs, tech startups, and annotation vendors. Apply through Rex.zone to find roles that match your experience level, employment type, and domain focus.

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AI Research Jobs in Canada — Remote Role

LinkedIn Job Metadata: Date Posted + 25-02-2026; Hiring Organization + Rexzone; Job Location + US; Workplace Type + Remote; Employment Type + FULL_TIME; Experience Level + Mid-Senior; Industry + Technology; Job Function + Engineering; Salary Currency + USD; Salary Range + 63360-126720; Pay Period + YEAR; Skills + AI research, machine learning research, large language models, RLHF, prompt evaluation, model evaluation, experiment design, training data quality, NLP, computer vision Role Overview: This Remote, FULL_TIME AI Research position supports end-to-end research and applied experimentation for modern AI systems, including LLM training pipelines, RLHF evaluation, and prompt evaluation. You will design experiments, define evaluation protocols, and improve model performance through robust measurement, dataset strategy, and QA evaluation workflows. Work may involve collaboration with AI labs, tech startups, and data operations partners, ensuring training data quality, annotation guidelines compliance, and safe deployment standards across NLP, computer vision, and content safety labeling tasks. Key Responsibilities: ["Develop and run AI research experiments across NLP, CV, and multimodal settings", "Design evaluation harnesses for large language model evaluation, including offline/online metrics", "Define RLHF and preference modeling evaluation protocols (rubrics, sampling plans, inter-rater reliability)", "Partner with data labeling teams to improve training data quality and annotation guidelines compliance", "Perform error analysis, ablation studies, and prompt evaluation to identify failure modes", "Build reproducible pipelines for dataset curation, data labeling audits, and QA evaluation", "Translate research findings into engineering-ready requirements and measurable acceptance criteria", "Support content safety labeling strategy and risk analysis for model outputs"] Required Skills: ["AI research and machine learning research fundamentals", "Experience with large language models and model evaluation", "Hands-on RLHF concepts (preference data, reward modeling, evaluation)", "Prompt evaluation and systematic rubric-based grading", "Strong experiment design, statistics basics, and model performance improvement workflows", "Familiarity with training data quality practices and dataset documentation", "NLP and/or computer vision domain knowledge", "Ability to collaborate cross-functionally with engineering and data operations"] Nice to Have: ["Experience building evaluation dashboards or automated QA evaluation tooling", "Knowledge of named entity recognition and structured prediction evaluation", "Exposure to content safety labeling and policy-based evaluation", "Experience working with annotation vendors or large-scale data labeling programs", "Applied research experience shipping models into production"] Work Modifiers Covered: ["Remote", "Full-time", "Contract", "Freelance", "Entry-level", "Senior", "NLP", "Computer vision", "Content safety", "LLM training", "AI labs", "Tech startups", "BPOs", "Annotation vendors"] How to Apply: ["Search Rex.zone for the latest AI research jobs in Canada", "Review role requirements and confirm Remote and FULL_TIME fit", "Submit your application with a research portfolio, publications, or experiment write-ups", "Highlight experience with RLHF, prompt evaluation, and model evaluation frameworks"]

Frequently Asked Questions

  • Q: What do AI research jobs in Canada typically involve?

    They typically involve designing and running experiments, improving model performance, and building evaluation methods for ML systems. Many roles include LLM training pipelines, prompt evaluation, RLHF evaluation, and collaboration with data labeling teams to ensure training data quality and annotation guidelines compliance.

  • Q: Are these AI research jobs remote and full-time?

    This page is optimized for Remote, FULL_TIME roles. Listings on Rex.zone may also include contract, freelance, entry-level, and senior variations depending on the employer and project needs.

  • Q: Which domains are most common for AI research roles on this page?

    Common domains include NLP, computer vision, multimodal learning, content safety labeling, and large language model evaluation. Roles frequently emphasize evaluation rigor, dataset strategy, and QA evaluation at scale.

  • Q: What skills should I highlight to match AI research job intent?

    Highlight AI research, machine learning research, large language models, model evaluation, experiment design, RLHF, and prompt evaluation. Also emphasize training data quality, error analysis, and collaboration with data labeling or QA evaluation workflows.

  • Q: What types of companies hire for AI research jobs in Canada?

    Employers commonly include AI labs, tech startups, enterprise AI teams, BPOs, and annotation vendors. Responsibilities can span research, research engineering, and applied evaluation for LLM training pipelines.

  • Q: How do I apply through Rex.zone?

    Use Rex.zone to browse AI research jobs in Canada, filter by Remote and FULL_TIME, and apply with a resume plus relevant research artifacts such as papers, technical reports, repositories, or evaluation write-ups demonstrating model performance improvement.

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