Senior Data Labeling Jobs Toronto

Senior data labeling roles at Rex.zone support AI/ML training workflows by producing high-quality labeled datasets, RLHF preference data, and evaluation benchmarks for large language models. You will apply annotation guidelines compliance, training data quality checks, and QA evaluation to drive model performance improvement across NLP, computer vision annotation, and content safety labeling. This remote, full-time opportunity connects your labeling expertise to real-world LLM training pipelines, prompt evaluation, and named entity recognition tasks while collaborating with cross-functional engineering teams at Rex.zone.

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Job Heading: Senior Data Labeling Jobs Toronto

Title: Senior Data Labeling Specialist (Toronto) Date: 25-02-2026 Company: Rex.zone Country: US Remote Type: Remote Employment Type: FULL_TIME Experience Level: Mid-Senior Industry: Technology Job Function: Engineering Skills: Senior data labeling, training data quality, annotation guidelines compliance, QA evaluation, RLHF, prompt evaluation, large language model 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

As a Senior Data Labeling Specialist focused on Toronto hiring intent, you will deliver high-accuracy annotations and evaluations used to train and validate AI systems. Your work will cover NLP labeling (including named entity recognition), RLHF preference ranking, prompt evaluation, and structured QA evaluation to improve dataset consistency and model outputs. You will interpret evolving annotation guidelines, resolve ambiguous edge cases, and document decisions that strengthen training data quality and reduce label noise.

Key Responsibilities

You will execute complex data labeling and evaluation tasks across text, image, and multimodal data. You will perform RLHF comparisons, write concise rationales, and flag failure modes that impact large language model evaluation. You will run QA audits, track inter-annotator agreement, calibrate annotators to shared standards, and maintain annotation guidelines compliance. You will partner with engineering and data ops to improve labeling workflows, sampling strategies, and feedback loops for model performance improvement.

Typical Workstreams

NLP: named entity recognition, intent labeling, taxonomy alignment, and error analysis. LLM: prompt evaluation, instruction-following grading, safety policy classification, and RLHF preference data creation. CV: computer vision annotation including bounding boxes, polygons, keypoints, and attribute tagging (as needed). Content safety labeling: policy-based classification, nuanced edge-case handling, and escalation notes for sensitive content.

Requirements

You have strong experience with data labeling at scale and proven ability to maintain training data quality under tight ambiguity constraints. You can apply annotation guidelines compliance, perform QA evaluation, and communicate clear reasoning for difficult judgments. You are comfortable working with LLM training pipelines, RLHF workflows, and large language model evaluation rubrics. You have strong written English skills, attention to detail, and the ability to work independently in a remote setting.

Preferred Qualifications

Experience with prompt evaluation frameworks, safety policy development, and structured rubric design. Familiarity with NLP concepts such as named entity recognition and semantic similarity, and/or CV labeling standards for computer vision annotation. Experience building QA processes, auditing labels, and improving inter-annotator agreement. Prior work with AI labs, tech startups, BPOs, or annotation vendors is a plus.

Tools and Collaboration

You will use web-based annotation tools, rubric checklists, and QA sampling workflows. Collaboration includes asynchronous reviews, calibration sessions, and clear documentation of edge cases. You will coordinate with engineering and data operations to improve throughput without compromising annotation guidelines compliance.

Why Rex.zone

Rex.zone connects skilled annotators and evaluators to real AI/ML production work. You will contribute directly to model performance improvement through training data quality, RLHF evaluation, and content safety labeling—while staying remote and full-time.

How to Apply

Apply through Rex.zone with a concise summary of your data labeling experience, domains (NLP, computer vision annotation, content safety labeling), and any RLHF or prompt evaluation work. Include examples of QA evaluation methods you have used to improve training data quality.

Frequently Asked Questions

  • Q: Is this a remote job even though the keyword includes Toronto?

    Yes. This role is explicitly Remote. The “Toronto” keyword reflects recruiting and search intent for senior data labeling jobs associated with Toronto candidates, while the work is performed remotely.

  • Q: What does “senior data labeling” mean in practice?

    It means handling complex labeling and evaluation tasks, resolving ambiguous edge cases, supporting annotation guidelines compliance, and contributing to QA evaluation and training data quality improvements that affect model performance improvement.

  • Q: Will I work on RLHF and large language model evaluation?

    Yes. Core workstreams include RLHF preference labeling, prompt evaluation, and large language model evaluation using structured rubrics, plus QA checks to ensure consistent judgments.

  • Q: Do you also label computer vision and content safety data?

    Potentially. Depending on project needs, you may support computer vision annotation and content safety labeling alongside NLP tasks like named entity recognition.

  • Q: Is this full-time and are contract or freelance options available?

    This posting is FULL_TIME. Rex.zone may also host contract, freelance, entry-level, and senior roles across AI data operations, but this role is specifically full-time.

  • Q: What industries and employer types does Rex.zone work with?

    Projects may come from AI labs, tech startups, enterprise teams, BPOs, and annotation vendors, with workflows spanning LLM training pipelines, evaluation, and data labeling.

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