[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"job-entry-level-stem-jobs-canada":3},{"Ques":4,"Slug":31,"Header":32,"job_category":60},{"title":5,"content":6},"Frequently Asked Questions",[7,10,13,16,19,22,25,28],{"A":8,"Q":9},"They are STEM-aligned roles focused on engineering and technology workflows, often tied to AI\u002FML training data operations such as data labeling, RLHF preference evaluation, QA evaluation, prompt evaluation, named entity recognition, computer vision annotation, and content safety labeling for LLM training pipelines.","What are entry level STEM jobs in Canada on Rex.zone?",{"A":11,"Q":12},"Yes. Remote roles must remain marked Remote, and this posting is Remote.","Is this role remote?",{"A":14,"Q":15},"The job metadata defaults are kept unchanged as required. The page targets the search intent for entry level STEM jobs in Canada while using the provided default job metadata values.","Why does the metadata show Country as US if the keyword is Canada?",{"A":17,"Q":18},"RLHF evaluation typically involves comparing model responses, ranking preferences, checking instruction-following, and applying rubrics so teams can generate reliable feedback signals to improve model performance in large language model training.","What does RLHF evaluation mean in practice?",{"A":20,"Q":21},"QA evaluation includes sampling labeled items, checking annotation guidelines compliance, tracking error types, measuring agreement, and correcting systematic issues to improve training data quality.","What is QA evaluation in data labeling?",{"A":23,"Q":24},"Not always. Strong STEM fundamentals, careful attention to detail, and comfort with structured rubrics are often sufficient. Familiarity with NLP, computer vision annotation, named entity recognition, or content safety labeling can help you ramp faster.","Do I need prior AI experience to apply?",{"A":26,"Q":27},"AI labs, tech startups, BPOs, and annotation vendors commonly hire for data labeling, content safety labeling, and large language model evaluation work that supports LLM training pipelines.","What kinds of employers hire for these workflows?",{"A":29,"Q":30},"You can commonly find remote, full-time, contract, freelance, entry-level, and senior roles, including domain-specific work in NLP, computer vision, content safety, and LLM training and evaluation.","What modifiers does Rex.zone support for job search?","entry level stem jobs canada",{"desc":33,"title":34,"content":35},"Entry level STEM jobs in Canada on Rex.zone focus on engineering and technology work that supports real AI\u002FML training workflows, including data labeling, RLHF evaluation, QA evaluation, prompt evaluation, named entity recognition, computer vision annotation, content safety labeling, and LLM training pipelines. You will collaborate with remote teams to follow annotation guidelines, improve training data quality, and drive model performance improvement across NLP and computer vision tasks. Explore full-time remote roles, plus contract and freelance pathways, across AI labs, tech startups, BPOs, and annotation vendors—then apply directly through Rex.zone.","Entry Level STEM Jobs in Canada",[36,39,42,45,48,51,54,57],{"h2":37,"desc":38},"Job Heading: Entry Level STEM Jobs in Canada","LinkedIn Job Metadata (Compact)\nTitle: Entry Level STEM Jobs in Canada\nDate: 25-02-2026\nCompany: Rexzone\nCountry: US\nRemote Type: Remote\nEmployment Type: FULL_TIME\nExperience Level: Mid-Senior\nIndustry: Technology\nJob Function: Engineering\nSkills: STEM fundamentals, engineering analysis, Python, SQL, data labeling, RLHF, QA evaluation, prompt evaluation, named entity recognition, NLP, computer vision annotation, content safety labeling, annotation guidelines, training data quality, LLM evaluation, model performance improvement\nSalary Currency: USD\nSalary Min: 63360\nSalary Max: 126720\nPay Period: YEAR",{"h2":40,"desc":41},"About the Role","You will support AI\u002FML development by producing and evaluating high-quality training data. Typical work includes data labeling for NLP and computer vision, prompt evaluation for large language model behavior, RLHF-style preference judgments, and QA evaluation to ensure annotation guidelines compliance. You will help improve training data quality, identify edge cases, document taxonomy decisions, and contribute to model performance improvement by providing clear feedback signals to LLM training pipelines.",{"h2":43,"desc":44},"What You Will Do","You will execute labeling and evaluation tasks across text, image, and multimodal datasets. You will perform named entity recognition, content classification, and content safety labeling; review model outputs during large language model evaluation; run QA sampling and discrepancy analysis; write issue notes that explain ambiguity and guideline gaps; and collaborate with leads to refine annotation guidelines, rubrics, and acceptance criteria.",{"h2":46,"desc":47},"Workflow and Tools","You will work in structured queues with clear rubrics, golden sets, and inter-annotator agreement targets. You may use web-based annotation platforms, spreadsheets, and basic scripting for checks. You will follow versioned guidelines, track QA evaluation outcomes, and participate in calibration sessions to align judgments for RLHF and prompt evaluation tasks.",{"h2":49,"desc":50},"Required Skills","You should be comfortable with STEM problem solving, careful reading, and consistent decision-making. Practical skills include basic Python and SQL, dataset hygiene, attention to detail, and clear written communication. Familiarity with NLP, computer vision annotation, named entity recognition, or content safety labeling is helpful, along with an ability to interpret rubrics and maintain annotation guidelines compliance.",{"h2":52,"desc":53},"Nice to Have","Experience with QA evaluation, audit sampling, or disagreement resolution; exposure to RLHF concepts and preference ranking; understanding of LLM training pipelines and large language model evaluation; and knowledge of common failure modes such as hallucinations, policy non-compliance, unsafe content, and prompt injection patterns.",{"h2":55,"desc":56},"Employment Details","Remote Type: Remote. Employment Type: FULL_TIME. This page targets entry level STEM jobs in Canada intent while listing remote opportunities on Rex.zone. Contract, freelance, and senior pathways may also be available depending on project needs and client requirements.",{"h2":58,"desc":59},"How to Apply on Rex.zone","Create or update your Rex.zone profile, highlight STEM coursework or projects, and emphasize relevant skills like data labeling, QA evaluation, RLHF, NLP, computer vision annotation, and content safety labeling. Submit your application through Rex.zone and be prepared for short guideline comprehension checks, calibration tasks, and quality reviews.","Engineering"]