Senior Data Annotator Jobs in London

Senior data annotator jobs in London focus on creating high-quality training data for AI/ML systems through data labeling, RLHF (Reinforcement Learning from Human Feedback), and QA evaluation across NLP, computer vision, and content safety workflows. At Rex.zone, you will apply annotation guidelines compliance, perform prompt evaluation and large language model evaluation, and improve training data quality to drive model performance improvement in real-world LLM training pipelines. Explore this full-time remote role to contribute to scalable annotation operations used by AI labs, tech startups, and annotation vendors.

Senior Data Annotator Jobs in London

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: Data annotation, data labeling, RLHF, QA evaluation, prompt evaluation, named entity recognition, computer vision annotation, content safety labeling, LLM evaluation, annotation guidelines compliance, training data quality Salary Currency: USD Salary Min: 63360 Salary Max: 126720 Pay Period: YEAR

About the Role

You will lead day-to-day annotation workstreams for LLM training pipelines, producing gold-standard labels and high-signal preference data for RLHF. The role spans data labeling, prompt evaluation, and QA evaluation to support model performance improvement in large language model evaluation and safety tuning. You will operate within Rex.zone workflows used by AI labs, tech startups, BPOs, and annotation vendors, ensuring training data quality and consistent annotation guidelines compliance across projects in NLP, computer vision, and content safety labeling.

What You Will Do

You will execute and review data annotation tasks across text, image, and multimodal datasets. You will create and refine labeling taxonomies, edge-case definitions, and decision rubrics. You will perform RLHF preference ranking, pairwise comparisons, and rubric-based scoring. You will run QA evaluation, error analysis, and inter-annotator agreement checks. You will complete prompt evaluation for instruction-following, helpfulness, and harmlessness. You will label and validate named entity recognition datasets and entity linking rules. You will contribute to content safety labeling for toxicity, self-harm, hate, and policy compliance. You will document annotation guidelines compliance and maintain change logs for schema updates. You will collaborate with engineering and data operations teams to improve tooling and throughput. You will propose process improvements that increase training data quality and reduce rework.

Core Domains You May Work In

NLP annotation: intent, sentiment, topic, summarization, groundedness, and factuality checks. LLM evaluation: rubric scoring, preference data, prompt-response grading, and calibration. RLHF: reward modeling datasets, pairwise ranking, and policy-aligned evaluations. Computer vision annotation: bounding boxes, polygons, keypoints, segmentation masks, and attributes. Content safety labeling: policy categories, severity scoring, and sensitive content handling. Information extraction: named entity recognition, relation extraction, and slot filling.

Requirements

Mid-senior experience in data annotation, data labeling, or AI/ML evaluation. Demonstrated ability to follow and improve annotation guidelines compliance. Experience with RLHF workflows, preference ranking, or rubric-based evaluation. Strong writing and reasoning skills for prompt evaluation and LLM evaluation. Comfort with QA evaluation methods (sampling plans, audit trails, consistency checks). Familiarity with named entity recognition and structured labeling schemas. Exposure to computer vision annotation or content safety labeling is a plus. Ability to work independently in a remote environment with high attention to detail.

Tools and Workflow

Annotation platforms, task queues, and audit tooling used in production labeling. Spreadsheet-style review, disagreement resolution, and adjudication workflows. Taxonomy versioning, guideline updates, and annotation change management. Basic data handling for spot checks and QA evaluation (e.g., CSV review and filters). Communication workflows for escalations, edge cases, and policy clarifications.

Success Metrics

Training data quality improvements measured through defect rate reduction and audit pass rates. High agreement and stable calibration across annotators and reviewers. Faster turnaround without sacrificing annotation guidelines compliance. Clear documentation of edge cases, rubric updates, and QA evaluation outcomes. Measurable model performance improvement signals tied to evaluation datasets.

Employment and Work Style

Remote: This role is explicitly Remote. Employment Type: FULL_TIME. You will collaborate across time zones with structured handoffs and documented decisions. You will work on projects spanning contract, freelance, and full-time annotation pipelines across the Rex.zone ecosystem, while this position is full-time.

How to Apply

Apply through Rex.zone and include a short summary of your annotation experience (NLP, computer vision, RLHF, QA evaluation, content safety labeling). If available, share examples of guideline writing, adjudication notes, or evaluation rubrics you have used to improve training data quality.

Frequently Asked Questions

Q: What are senior data annotator jobs in London focused on?
They focus on producing high-quality training data for AI/ML systems via data labeling, RLHF preference data, QA evaluation, and prompt evaluation. Typical work includes annotation guidelines compliance, error analysis, and large language model evaluation to support model performance improvement.
Q: Is this role remote and full-time?
Yes. The Remote Type is Remote and the Employment Type is FULL_TIME, and the workflow is designed for remote delivery with clear QA evaluation and review processes.
Q: What domains are commonly covered in this role?
Common domains include NLP labeling (classification, summarization, extraction), named entity recognition, computer vision annotation (boxes, polygons, segmentation), content safety labeling, and RLHF and LLM evaluation workflows.
Q: What is RLHF and how does it relate to data annotation?
RLHF (Reinforcement Learning from Human Feedback) uses human preference data and rubric-based scoring to train reward models and align LLM behavior. Senior annotators generate high-signal preference labels and ensure consistency through calibration and QA evaluation.
Q: What skills should align with this job posting intent?
Skills should align with senior data annotation and evaluation workflows, including data labeling, RLHF, QA evaluation, prompt evaluation, named entity recognition, computer vision annotation, content safety labeling, LLM evaluation, annotation guidelines compliance, and training data quality.
Q: What types of employers use these workflows?
AI labs, tech startups, BPOs, and annotation vendors commonly use these workflows to build scalable LLM training pipelines and evaluation datasets.
Q: How is quality measured in senior annotation work?
Quality is measured through training data quality metrics such as audit pass rates, defect rates, inter-annotator agreement, adjudication outcomes, and downstream model performance improvement signals.
Q: Where does Rex.zone fit into the process?
Rex.zone provides the platform context for remote data annotation work, including task routing, QA evaluation workflows, guideline distribution, and review/adjudication loops used to support LLM training pipelines.

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