Senior Data Labeling Jobs in Ottawa (Remote)

Rex.zone is hiring for Senior Data Labeling roles supporting AI/ML training workflows end to end, including data labeling, RLHF, prompt evaluation, QA evaluation, and training data quality audits for large language model evaluation and computer vision annotation. You will apply annotation guidelines compliance, resolve edge cases, and improve model performance through consistent labeling decisions across NLP, named entity recognition, content safety labeling, and multimodal datasets. This is a full-time remote opportunity aligned with Ottawa talent, built for experienced annotators who can mentor peers, triage quality issues, and keep LLM training pipelines reliable from sampling to final acceptance.

Job Overview

Keyword + Job Title: Senior Data Labeling Jobs in Ottawa (Remote) Date Posted: 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, RLHF, prompt evaluation, QA evaluation, annotation guidelines compliance, training data quality, 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 Rex.zone

Rex.zone connects skilled labeling talent with teams building production AI systems across NLP, computer vision, and content safety. We prioritize measurable training data quality, clear annotation guidelines, and repeatable QA evaluation so model teams can trust their datasets. Our projects commonly support LLM training pipelines, RLHF preference data, prompt evaluation, and evaluation datasets for model performance improvement.

What You Will Do

Execute senior-level data labeling across text, image, and multimodal tasks with consistent decision-making and high throughput without sacrificing accuracy. Perform RLHF-style preference labeling and ranking, including pairwise comparisons and rationale capture aligned to rubric-based guidelines. Run prompt evaluation and QA evaluation to identify failure modes, ambiguity, and policy gaps that affect large language model evaluation. Audit training data quality by sampling, error categorization, and root-cause analysis (guideline gaps, tooling issues, edge-case drift). Ensure annotation guidelines compliance by flagging unclear instructions, proposing clarifications, and updating examples for edge cases. Support named entity recognition and span labeling (entities, attributes, relations) with consistent schema adherence and boundary rules. Contribute to content safety labeling (policy categories, severity, context) to reduce false positives/negatives in safety classifiers. Coordinate with project leads to resolve disagreements, calibrate annotator decisions, and stabilize inter-annotator agreement targets. Document decisions, escalation notes, and acceptance criteria so downstream model training teams can interpret labels correctly.

Core Workflows You Will Touch

Dataset intake, sampling plans, and label taxonomy mapping for new domains and evolving model objectives. Gold set creation, consensus labeling, and disagreement analysis to improve labeling consistency and model performance improvement. Quality systems: spot checks, batch-level acceptance, error rate tracking, and corrective action plans. Evaluation datasets: prompt sets, adversarial prompts, safety probes, and rubric-driven model outputs review. Computer vision annotation: bounding boxes, polygons, keypoints, segmentation masks, and attribute tagging where applicable. NLP labeling: classification, intent, sentiment, NER, summarization evaluation, and instruction-following evaluation. Tooling feedback loops: UI friction reporting, shortcut recommendations, and annotation speed/quality tradeoff optimization.

Who You Are

You have strong experience in data labeling or data annotation with a demonstrated ability to handle complex edge cases and ambiguous policy contexts. You can read and apply detailed rubrics, maintain annotation guidelines compliance, and communicate when guidelines conflict with real data. You are comfortable with RLHF, prompt evaluation, and QA evaluation concepts even when tasks are non-technical in execution. You can mentor peers through calibration sessions, provide actionable feedback, and raise dataset risks early. You write clear notes that help reviewers, QA, and model teams understand why labels were chosen.

Preferred Background (Not Required)

Experience with large language model evaluation, rubric design, or preference data collection. Exposure to content safety labeling policies and structured severity/intent frameworks. Prior work in annotation vendors, BPO environments, AI labs, or tech startups with fast-changing labeling specs. Hands-on familiarity with computer vision annotation and multi-pass QA processes. Experience tracking training data quality metrics such as disagreement rate, rework rate, and gold accuracy.

Employment Details

Remote Type: Remote (must remain Remote) Employment Type: FULL_TIME Location Targeting: Ottawa-aligned talent (role is remote; team may include distributed stakeholders) Compensation Range: 63360 to 126720 USD per YEAR (based on scope, domain complexity, and quality ownership)

How to Apply on Rex.zone

Apply through Rex.zone with an updated resume highlighting senior data labeling experience and quality ownership. Include examples of task types you have labeled (NLP, computer vision annotation, content safety labeling, RLHF, prompt evaluation). If available, share metrics you influenced: training data quality improvements, reduced rework, increased inter-annotator agreement, or faster QA evaluation cycles.

Frequently Asked Questions

Q: Is this a remote role even though it targets Ottawa?
Yes. The Remote Type is Remote. The Ottawa modifier reflects the target talent market and search intent, while day-to-day work is performed remotely.
Q: What makes these senior data labeling jobs different from entry-level labeling?
Senior data labeling emphasizes training data quality ownership, handling edge cases, calibration, disagreement resolution, and QA evaluation. You will also contribute to guideline improvements that directly impact model performance improvement.
Q: Will I work on LLM projects or computer vision annotation?
Projects may include large language model evaluation (RLHF, prompt evaluation, rubric scoring) and/or computer vision annotation (bounding boxes, polygons, segmentation). Assignment depends on your strengths and current demand.
Q: What skills are most important for success?
Strong annotation guidelines compliance, careful reasoning, consistency across edge cases, QA evaluation discipline, and familiarity with RLHF or prompt evaluation workflows are the most important.
Q: Is this full-time only, or are contract/freelance options available?
This posting is for FULL_TIME. Rex.zone may also list contract or freelance roles separately depending on project needs.
Q: What types of employers use Rex.zone talent?
Rex.zone supports employer types including AI labs, tech startups, annotation vendors, and BPO-style delivery teams working on NLP, content safety labeling, and LLM training pipelines.
Q: How is quality measured in these roles?
Quality is measured through gold set accuracy, spot-check pass rates, disagreement analysis, rework rate, and adherence to annotation guidelines. For LLM work, rubric consistency and evaluator calibration are also key.

230+Domains Covered

120K+PhD, Specialist, Experts Onboarded

50+Countries Represented

Industry-Leading Compensation

We believe exceptional intelligence deserves exceptional pay. Our platform consistently offers rates above the industry average, rewarding experts for their true value and real impact on frontier AI. Here, your expertise isn't just appreciated - it's properly compensated.

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