[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"job-stem-research-jobs-canada":3},{"Ques":4,"Slug":25,"Header":26,"job_category":57},{"title":5,"content":6},"Frequently Asked Questions",[7,10,13,16,19,22],{"A":8,"Q":9},"It refers to STEM research-oriented roles aligned to Canada-focused search intent, covering applied research and engineering work such as LLM training pipelines, large language model evaluation, and dataset development through data labeling and QA evaluation.","What does “STEM Research Jobs in Canada” mean on Rex.zone?",{"A":11,"Q":12},"Yes. The role is Remote and FULL_TIME, supporting distributed collaboration across research, engineering, and data operations teams.","Is this role remote and full-time?",{"A":14,"Q":15},"Common workflows include RLHF, prompt evaluation, training data quality audits, annotation guidelines compliance, named entity recognition labeling, computer vision annotation QA, and content safety labeling evaluation.","What types of AI\u002FML workflows are included?",{"A":17,"Q":18},"Highlight STEM research, applied machine learning, Python, experiment design, statistical analysis, NLP or computer vision experience, large language model evaluation, data labeling, QA evaluation, RLHF, prompt evaluation, and experience improving training data quality.","Which skills should I highlight to match this posting?",{"A":20,"Q":21},"It aligns with AI labs, tech startups, annotation vendors, and BPOs that support AI\u002FML data operations and evaluation programs.","What kinds of employers does this role align with?",{"A":23,"Q":24},"Success is measured by improved training data quality, stronger evaluation reliability, measurable model performance improvement, and reproducible research outputs that can be integrated into production pipelines.","How is performance evaluated in this role?","stem-research-jobs-canada",{"desc":27,"title":28,"content":29},"STEM Research Jobs in Canada at Rex.zone support applied AI\u002FML and software engineering research workflows, including LLM training pipelines, data labeling, RLHF evaluation, QA evaluation, and prompt evaluation. You will collaborate with cross-functional teams to build training datasets, run annotation guidelines compliance checks, and drive model performance improvement using NLP, computer vision, and content safety labeling methods. This remote, full-time role aligns with STEM research hiring needs across AI labs, tech startups, annotation vendors, and BPOs while keeping research outputs reproducible, measurable, and production-ready.","STEM Research Jobs in Canada",[30,33,36,39,42,45,48,51,54],{"h2":31,"desc":32},"Job Overview","Keyword: STEM Research Jobs in Canada | Job Title: STEM Research Engineer (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 research, applied machine learning, NLP, computer vision, RLHF, data labeling, QA evaluation, prompt evaluation, named entity recognition, content safety labeling, LLM training pipelines, experiment design, statistical analysis, Python\nSalary Currency: USD\nSalary Min: 63360\nSalary Max: 126720\nPay Period: YEAR",{"h2":34,"desc":35},"About the Role","As a STEM Research Engineer focused on STEM Research Jobs in Canada, you will contribute to applied research and engineering tasks that improve large language model evaluation, training data quality, and end-to-end AI\u002FML data operations. You will translate research questions into measurable experiments, create and validate datasets through data labeling and QA evaluation, and support RLHF workflows through prompt evaluation and human feedback aggregation. Your work will include annotation guidelines compliance, error analysis, and iterative dataset refinement to improve model performance across NLP and computer vision use cases, including named entity recognition and content safety labeling. You will document findings, build reproducible pipelines, and partner with engineering and product stakeholders to operationalize research outcomes on Rex.zone.",{"h2":37,"desc":38},"Key Responsibilities","[\"Design and execute applied STEM research experiments aligned to model performance improvement and deployment constraints\",\"Build and maintain LLM training pipelines that combine data labeling, RLHF signals, and QA evaluation outputs\",\"Develop prompt evaluation protocols and scoring rubrics for large language model evaluation across multiple task types\",\"Implement training data quality checks including annotation guidelines compliance, inter-annotator agreement analysis, and sampling audits\",\"Run error analysis on NLP and computer vision tasks, including named entity recognition and computer vision annotation edge cases\",\"Support content safety labeling workflows by defining taxonomies, adjudication rules, and escalation criteria\",\"Partner with AI labs, tech startups, and operations teams (including annotation vendors and BPOs) to standardize dataset formats and evaluation metrics\",\"Produce research documentation, experiment logs, and reproducible code to ensure traceability and scientific rigor\",\"Communicate results to technical and non-technical stakeholders with clear recommendations for dataset and evaluation improvements\"]",{"h2":40,"desc":41},"Required Qualifications","[\"Mid-Senior experience in STEM research, applied ML research, or engineering research roles\",\"Strong Python skills for data processing, experiment automation, and evaluation tooling\",\"Hands-on experience with at least one of: NLP, computer vision, or multimodal evaluation\",\"Knowledge of training data quality practices including sampling, QA evaluation, and annotation guidelines compliance\",\"Familiarity with RLHF concepts, prompt evaluation methods, and large language model evaluation metrics\",\"Ability to design experiments, interpret results, and present findings with statistical reasoning\",\"Experience collaborating in remote, cross-functional environments with clear written communication\"]",{"h2":43,"desc":44},"Preferred Qualifications","[\"Experience with named entity recognition datasets and evaluation (span-level labeling, adjudication, error taxonomy)\",\"Experience with computer vision annotation (bounding boxes, segmentation, keypoints) and related QA methods\",\"Exposure to content safety labeling policies, risk classification, and moderation evaluation frameworks\",\"Experience building or improving LLM training pipelines for instruction tuning, preference data, or evaluation benchmarks\",\"Familiarity with data governance, dataset versioning, and reproducible ML workflows\"]",{"h2":46,"desc":47},"Tools and Workflow","[\"Python, notebooks, and data pipelines for dataset preparation and evaluation automation\",\"Annotation platforms and QA tooling for data labeling and review workflows\",\"Experiment tracking, dataset versioning, and structured documentation for reproducibility\",\"Evaluation harnesses for prompt evaluation, RLHF scoring, and large language model evaluation\"]",{"h2":49,"desc":50},"How Success Is Measured","[\"Training data quality improvements demonstrated through QA evaluation pass rates and reduced label error\",\"Model performance improvement tied to dataset refinements, RLHF iteration quality, and evaluation stability\",\"Annotation guidelines compliance and adjudication consistency across reviewers\",\"Clear, reproducible research outputs that can be operationalized in production pipelines\"]",{"h2":52,"desc":53},"Remote Work and Employment Details","[\"Remote: Yes (Remote Type: Remote)\",\"Employment: FULL_TIME\",\"Level: Mid-Senior\",\"Function: Engineering\",\"Industry: Technology\",\"Compensation: USD 63360 to 126720 per year\"]",{"h2":55,"desc":56},"Apply on Rex.zone","Explore STEM Research Jobs in Canada on Rex.zone to find research-forward roles spanning NLP, computer vision, content safety labeling, and LLM training pipelines. Apply with a resume highlighting applied research impact, experiment design, and hands-on evaluation experience across data labeling, RLHF, QA evaluation, and prompt evaluation.","Engineering"]