Entry Level AI Jobs in Canada

Entry level AI jobs in Canada at Rex.zone focus on practical AI/ML data operations that power LLM training pipelines, RLHF, and model evaluation. In this remote full-time role, you will produce high-quality training data through data labeling, prompt evaluation, and QA evaluation, following annotation guidelines compliance to improve model performance. You will work across NLP and computer vision annotation tasks, apply content safety labeling policies, and support training data quality for AI labs, tech startups, and annotation vendors. Explore, apply, and grow your career on Rex.zone with structured workflows, clear rubrics, and measurable impact on large language model evaluation.

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Job Heading: Entry Level AI Jobs in Canada

Title: Entry Level AI Jobs in Canada Date: 25-02-2026 Company: Rexzone Country: US Remote Type: Remote Employment Type: FULL_TIME Experience Level: Mid-Senior Industry: Technology Job Function: Engineering Skills: RLHF, data labeling, QA evaluation, prompt 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 support end-to-end AI training workflows by labeling and evaluating data used to train and evaluate large language models. Your work will include RLHF-style preference ranking, prompt/response evaluation, and structured QA evaluation to ensure training data quality and annotation guidelines compliance. You will also complete NLP tasks such as named entity recognition and text classification, plus computer vision annotation such as bounding boxes and segmentation. This role is remote and full-time, aligned to the Rex.zone marketplace where teams source reliable contributors for scalable LLM training pipelines.

Key Responsibilities

You will deliver consistent, high-accuracy labels using rubric-based decisioning; perform RLHF comparisons and preference judgments to support model performance improvement; execute prompt evaluation for helpfulness, harmlessness, and truthfulness; complete QA evaluation by reviewing peer outputs and resolving edge cases; apply content safety labeling policies for toxic, self-harm, sexual, and violence categories; annotate NLP datasets including named entity recognition, sentiment, and intent; annotate computer vision datasets including classification, bounding boxes, polygons, and segmentation masks; document rationale and exceptions to strengthen annotation guidelines compliance; collaborate asynchronously with leads to calibrate and reduce inter-annotator disagreement; meet throughput and quality targets tied to training data quality.

Qualifications

You can follow detailed guidelines, handle ambiguous cases, and explain labeling rationale clearly. Familiarity with AI/ML concepts, LLMs, and evaluation metrics is helpful, but strong attention to detail and QA habits are essential. Experience with any of the following is relevant: data labeling platforms, prompt evaluation, RLHF ranking, named entity recognition, computer vision annotation tools, or content moderation policy application. You are comfortable working remotely, managing time independently, and maintaining consistent output quality.

Tools and Workflows You Will Use

You will use web-based annotation tools, rubric-driven QA checklists, and calibration tasks to align on labeling standards. Workflows may include gold-standard tasks, consensus labeling, adjudication, and audit sampling to improve training data quality. You will contribute to datasets used in LLM training pipelines, model evaluation, and safety tuning across NLP, CV, and content safety labeling domains.

What Success Looks Like

You maintain high agreement with guidelines, produce low-defect annotations, and consistently improve review scores over time. You reduce ambiguity by capturing edge cases and proposing guideline clarifications. Your RLHF and prompt evaluation decisions map cleanly to rubrics, enabling reliable large language model evaluation and model performance improvement. You hit throughput targets without sacrificing annotation guidelines compliance.

How to Apply on Rex.zone

Create or update your Rex.zone profile, highlight relevant skills (RLHF, data labeling, QA evaluation, prompt evaluation, named entity recognition, computer vision annotation, content safety labeling, LLM training pipelines), and submit your application for remote full-time opportunities. Keep your availability and domain preferences (NLP, computer vision, content safety) current to match with suitable projects and employer types such as AI labs, tech startups, BPOs, and annotation vendors.

Frequently Asked Questions

  • Q: Are these entry level AI jobs in Canada truly remote?

    Yes. This posting is explicitly Remote and is designed for distributed AI/ML data operations workflows such as data labeling, RLHF, prompt evaluation, and QA evaluation completed online through Rex.zone.

  • Q: Why does the listing say Country is US if the keyword is entry level AI jobs Canada?

    The role targets the search intent for entry level AI jobs in Canada, but the job metadata provided specifies Country: US and must remain unchanged per the posting rules.

  • Q: What kind of AI work is involved in this role?

    You will work on training data quality for LLM training pipelines, including RLHF preference ranking, prompt evaluation, QA evaluation, named entity recognition, computer vision annotation, and content safety labeling.

  • Q: Is this a contract or full-time position?

    This job is FULL_TIME per the metadata. Rex.zone may also host contract or freelance options, but this specific posting is full-time and remote.

  • Q: Do I need prior experience with machine learning to apply?

    You do not need to be a model builder, but you should be comfortable following detailed rubrics, applying policies consistently, and producing high-quality labels. Any exposure to NLP, computer vision annotation, or content moderation is helpful.

  • Q: What skills should I highlight for entry level AI jobs in Canada on Rex.zone?

    Highlight RLHF, data labeling, QA evaluation, prompt evaluation, named entity recognition, computer vision annotation, content safety labeling, and LLM training pipelines, plus evidence of accuracy, consistency, and guideline compliance.

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