Senior Data Labeling Jobs in London

Senior Data Labeling roles at Rex.zone focus on training data quality for AI/ML systems, combining data labeling, RLHF evaluation, and QA evaluation to improve large language model evaluation and downstream model performance improvement. You will apply annotation guidelines compliance across NLP and computer vision annotation tasks, including named entity recognition, prompt evaluation, and content safety labeling, while partnering with AI labs, tech startups, and annotation vendors. This is a Remote, full-time role designed for experienced annotators and labeling leads who can drive LLM training pipelines, resolve edge cases, and raise throughput without sacrificing accuracy. Explore and apply on Rex.zone to join production-grade evaluation workflows.

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Job Heading: Senior Data Labeling Specialist (London)

Title: Senior Data Labeling Specialist (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: senior data labeling, 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 supporting London-aligned teams and projects, you will lead high-precision labeling and evaluation work that powers LLM training pipelines. Your day-to-day will span RLHF preference comparisons, prompt evaluation, QA evaluation, and multi-domain annotation (NLP, computer vision annotation, and content safety labeling). You will help translate product and research goals into clear annotation guidelines compliance, improve training data quality, and surface systematic failure modes that unlock model performance improvement.

What You Will Do

You will execute and lead senior data labeling work across multiple task types while maintaining consistent quality at scale. Responsibilities include: creating and refining labeling rubrics; performing RLHF rankings and preference data creation; running QA evaluation checks and adjudication on disputed examples; performing named entity recognition and text classification; supporting computer vision annotation (bounding boxes, polygons, keypoints) when needed; conducting content safety labeling for policy-aligned datasets; documenting edge cases and escalating taxonomy gaps; and partnering with operations to improve throughput, inter-annotator agreement, and audit readiness.

Training Data Quality and Evaluation Workflows

This role is centered on training data quality within modern AI development loops. You will contribute to large language model evaluation by applying consistent decision logic, calibrating across annotators, and reducing label noise. You will work with sampling plans, gold data, and targeted audits to ensure annotation guidelines compliance. You will also support error analysis to connect labeling patterns to model performance improvement, especially on tasks involving instruction following, safety, and factuality.

Who You Are

You are a mid-senior practitioner who has shipped real labeling outcomes, not just performed isolated tasks. You can interpret ambiguous instructions, ask the right clarifying questions, and build shared understanding across stakeholders. You have strong written communication, careful reasoning, and the ability to maintain consistency across long labeling sessions. You are comfortable switching among NLP, content safety labeling, and computer vision annotation requirements, and you can mentor peers while staying hands-on.

Required Skills

Key requirements include senior data labeling experience, strong data labeling fundamentals, and proven accuracy in QA evaluation. You should be comfortable with RLHF style preference labeling, prompt evaluation, and large language model evaluation criteria. Familiarity with named entity recognition, taxonomy design, annotation guidelines compliance, and training data quality measurement (agreement, precision/recall thinking, audit processes) is expected. Exposure to content safety labeling and/or computer vision annotation is a strong advantage.

Preferred Experience

Preferred qualifications include prior work with AI labs, tech startups, BPOs, or annotation vendors; experience building gold standards and calibration sets; experience with escalation workflows and adjudication; and experience mapping labeling outcomes to model performance improvement. Exposure to multilingual data or region-specific policy interpretation (including UK/London market context) is helpful, but the role remains Remote and US-based for employment details.

Employment Details

This is a Remote, FULL_TIME role at Rex.zone with Mid-Senior experience level. Compensation range is USD 63360 to 126720 per YEAR. The work may include projects across NLP, computer vision annotation, and content safety labeling domains, depending on client and model needs. Apply through Rex.zone to be considered for active and upcoming senior data labeling pipelines.

How to Apply on Rex.zone

To apply, prepare a concise summary of your senior data labeling background, including task types (RLHF, QA evaluation, prompt evaluation, named entity recognition, computer vision annotation, content safety labeling) and examples of how you improved training data quality or annotation guidelines compliance. Then submit your application through Rex.zone and be ready to complete a short calibration or evaluation sample aligned to large language model evaluation workflows.

Frequently Asked Questions

  • Q: Is this a remote role even though it targets London senior data labeling jobs?

    Yes. The position is explicitly Remote. The London keyword reflects project alignment and search intent, while employment details remain US-based as specified in the metadata.

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

    It means you are trusted with complex edge cases, adjudication, calibration, and improving annotation guidelines compliance. You will influence training data quality and support large language model evaluation and model performance improvement, not just complete straightforward labeling queues.

  • Q: What task types will I work on?

    Common tasks include RLHF preference labeling, prompt evaluation, QA evaluation, named entity recognition, content safety labeling, and sometimes computer vision annotation depending on project needs.

  • Q: Do I need engineering experience since Job Function is Engineering?

    You do not need to be a software engineer, but you should be comfortable with rigorous evaluation logic, structured workflows, and high-precision quality processes. Many teams classify advanced labeling and evaluation roles under Engineering due to their impact on LLM training pipelines.

  • Q: How is quality measured?

    Quality is typically measured through gold data accuracy, inter-annotator agreement, targeted audits, calibration performance, and consistency with annotation guidelines compliance. Strong performers also contribute to reducing systematic label noise that affects model performance improvement.

  • Q: Is this full-time and what is the pay range?

    Yes, it is FULL_TIME. The salary range is USD 63360 to 126720 per YEAR.

  • Q: What types of companies might I support through Rex.zone?

    Projects can come from AI labs, tech startups, enterprise teams, BPOs, and annotation vendors, covering NLP, content safety labeling, computer vision annotation, and large language model evaluation.

  • Q: What should I include in my application?

    Include your relevant task history (data labeling, RLHF, QA evaluation, prompt evaluation, named entity recognition, content safety labeling, computer vision annotation), examples of training data quality improvements, and how you handled ambiguous cases while maintaining annotation guidelines compliance.

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