Senior Data Annotator Jobs Manchester

Senior Data Annotator jobs Manchester on Rex.zone focus on producing high-quality training data for AI/ML systems through data labeling, RLHF evaluation, QA review, and prompt evaluation workflows. In this remote, full-time role, you will apply annotation guidelines compliance to improve training data quality for large language model evaluation, NLP named entity recognition, computer vision annotation, and content safety labeling. You will collaborate with cross-functional engineering teams to support LLM training pipelines, measure model performance improvement, and deliver consistent, auditable labels at scale for technology clients.

Job Overview — Senior Data Annotator Jobs Manchester

About Rex.zone

Rex.zone is a remote-first platform where professionals explore AI/ML data operations opportunities, including full-time and contract work streams. Teams support AI labs, tech startups, and annotation vendors by executing scalable data labeling, RLHF, and evaluation programs that drive measurable model performance improvement.

What You Will Do

Deliver expert-level data annotation across NLP, LLM evaluation, and computer vision annotation tasks; perform RLHF preference ranking and prompt evaluation for instruction-following and safety; execute QA evaluation to ensure training data quality; enforce annotation guidelines compliance and document edge cases; run second-pass reviews, adjudicate disagreements, and provide calibrated feedback to annotators; support content safety labeling (policy, harm categories, refusals, and sensitive content); collaborate with engineering on schema updates, tooling feedback, and sampling plans to improve throughput and label consistency; track error patterns and contribute to model performance improvement through high-signal datasets.

Core Workstreams (NLP, CV, RLHF, Safety)

NLP named entity recognition and text classification; LLM evaluation including instruction quality, factuality checks, and rubric-based scoring; RLHF pairwise preference and ranking data creation; computer vision annotation such as bounding boxes, polygons, keypoints, and segmentation; content safety labeling aligned to policy definitions and enforcement thresholds.

Required Qualifications

Mid-Senior experience delivering production data annotation or QA evaluation; strong understanding of annotation guidelines compliance and ambiguity resolution; proven ability to produce consistent labels under time constraints while maintaining training data quality; experience with at least one of: RLHF, prompt evaluation, named entity recognition, computer vision annotation, or content safety labeling; comfort working with web-based annotation tools, spreadsheets, and issue trackers; clear written communication for edge-case notes and reviewer feedback.

Preferred Qualifications

Experience evaluating large language model outputs using rubrics and audit trails; familiarity with LLM training pipelines and how labeled data impacts model performance improvement; experience mentoring annotators, designing calibration sets, or running inter-annotator agreement checks; exposure to multi-domain datasets (NLP + CV + safety) and multilingual labeling; knowledge of data privacy and secure handling requirements for sensitive content.

Quality Standards and QA Evaluation

You will apply sampling and review workflows to maintain training data quality, including second-pass QA, adjudication, and targeted rework. Success is measured by guideline adherence, low defect rates, consistent reasoning notes, and reliable outputs that support downstream LLM evaluation and model performance improvement.

Work Arrangement and Employment Details

Remote Type: Remote; Employment Type: FULL_TIME; Experience Level: Mid-Senior; Country: US. This role supports distributed delivery for clients that may include AI labs, technology companies, BPOs, and annotation vendors. Work may span projects labeled as remote, contract, freelance, full-time, entry-level, or senior depending on business needs; this posting is for full-time remote senior work.

Compensation

Salary Currency: USD; Salary Min: 63360; Salary Max: 126720; Pay Period: YEAR. Compensation may vary based on evaluation scope (RLHF vs. CV), QA responsibilities, domain complexity, and demonstrated annotation guidelines compliance.

How to Apply on Rex.zone

Search and apply for Senior Data Annotator jobs Manchester on Rex.zone by completing your profile, highlighting relevant data labeling and QA evaluation experience, and listing domain strengths (NLP, computer vision annotation, RLHF, content safety labeling). Include examples of rubric-based LLM evaluation or training data quality improvements when available.

Frequently Asked Questions

Q: Is this role remote and full-time?
Yes. The posting is explicitly Remote and FULL_TIME, supporting distributed delivery through Rex.zone.
Q: Why does the page say Manchester if the country is US?
The keyword targets “Senior Data Annotator jobs Manchester” as an SEO job-search query, while the role metadata specifies Country: US and Remote Type: Remote. The work is remote and not limited by city, but the page is optimized for that search intent.
Q: What kinds of tasks are included in senior data annotation?
Typical tasks include data labeling, QA evaluation, annotation guidelines compliance, RLHF preference ranking, prompt evaluation, named entity recognition, computer vision annotation, and content safety labeling to support LLM training pipelines.
Q: What is RLHF and why does it matter here?
RLHF (Reinforcement Learning from Human Feedback) is a workflow where human judgments create preference data used to align model behavior. In this role, RLHF labels improve model performance improvement by guiding large language model evaluation and tuning.
Q: What tools will I use?
Most work is done in web-based annotation tools plus spreadsheets and issue trackers. Specific tooling varies by client dataset and domain (NLP, CV, or safety).
Q: How is quality measured?
Quality is measured through QA evaluation processes such as second-pass review, adjudication, calibration, defect-rate monitoring, and evidence that your labels improve training data quality and downstream model performance.
Q: What skills should I emphasize when applying?
Emphasize data annotation, data labeling, RLHF, quality assurance, prompt evaluation, named entity recognition, computer vision annotation, content safety labeling, LLM evaluation, annotation guidelines compliance, training data quality, and familiarity with LLM training pipelines.
Q: Is this suitable for entry-level applicants?
This posting targets Mid-Senior experience. Rex.zone may list entry-level roles separately, but this role expects strong guideline adherence, reliable QA output, and the ability to handle complex edge cases.

230+Domains Covered

120K+PhD, Specialist, Experts Onboarded

50+Countries Represented

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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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