Senior Data Labeling Jobs in Warsaw (Remote)

Senior Data Labeling jobs in Warsaw on Rex.zone focus on building high-quality training datasets for modern AI systems. In this Mid-Senior, FULL_TIME Remote role at Rex.zone, you will lead data labeling workflows across LLM training pipelines, RLHF evaluation, prompt evaluation, content safety labeling, and QA evaluation. You will apply annotation guidelines compliance, drive training data quality, and partner with engineers to deliver model performance improvement for NLP and computer vision annotation programs. If you are looking for remote, full-time senior data labeling work aligned with AI labs, tech startups, and annotation vendors, this posting is designed to help you apply through Rex.zone.

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Job Overview

Keyword + Job Title: Senior Data Labeling Jobs in Warsaw (Remote) Title: Senior Data Labeling Jobs in Warsaw (Remote) 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 labeling, RLHF, QA evaluation, Prompt evaluation, Annotation guidelines, Training data quality, LLM evaluation, Named entity recognition, Computer vision annotation, Content safety labeling Salary Currency: USD Salary Min: 63360 Salary Max: 126720 Pay Period: YEAR

About Rex.zone and Rex.zone

Rex.zone connects candidates with remote AI/ML data operations work, including data labeling, RLHF evaluation, and model evaluation roles. Rex.zone runs structured annotation programs that support large language model evaluation, content safety labeling, and multimodal datasets (text, image, and video) used in production training pipelines.

What You Will Do

You will lead end-to-end data labeling operations for NLP and computer vision annotation programs, ensuring training data quality and consistency across datasets. You will execute and review RLHF tasks, including preference ranking, prompt evaluation, and rubric-based QA evaluation. You will enforce annotation guidelines compliance, calibrate raters, resolve edge cases, and maintain clear decision logs. You will run sampling audits, track error taxonomies, and drive continuous model performance improvement feedback to engineering. You will coordinate with stakeholders across AI labs, tech startups, BPOs, and annotation vendors to deliver on throughput, accuracy, and safety targets.

Key Workstreams (Entity + Semantic Coverage)

Large language model evaluation: instruction-following, factuality, helpfulness/harmlessness, and style compliance. RLHF (Reinforcement Learning from Human Feedback): pairwise ranking, preference modeling signals, and rater calibration. QA evaluation: inter-annotator agreement, gold set management, and audit sampling. Prompt evaluation: adversarial prompts, prompt-response grading, and rubric refinement. Named entity recognition: entity span labeling, ontology management, and ambiguity resolution. Computer vision annotation: bounding boxes, polygons, keypoints, segmentation masks, and attribute tagging. Content safety labeling: hate/harassment, self-harm, sexual content, and policy-driven severity rating. LLM training pipelines: dataset versioning, drift detection, and training-ready data packaging.

Required Qualifications

Mid-Senior experience in data labeling, data annotation, or AI/ML evaluation. Demonstrated ability to apply and improve annotation guidelines, rubrics, and taxonomies with measurable training data quality outcomes. Experience with QA evaluation methods such as audits, gold data, and inter-annotator agreement. Familiarity with RLHF-style workflows, preference ranking, prompt evaluation, and LLM evaluation concepts. Strong written communication for documenting decisions, edge cases, and annotation policies.

Preferred Qualifications

Experience leading annotation teams or acting as a QA lead, validator, or project lead. Background in NLP labeling (NER, text classification) and/or computer vision annotation (segmentation, detection). Exposure to content safety labeling, policy interpretation, and risk-based QA. Experience collaborating with engineering on data pipelines, dataset versioning, or evaluation dashboards.

Tools and Working Style

Comfort working in remote, metrics-driven annotation operations with a focus on quality, speed, and reproducibility. Ability to manage annotation queues, perform structured reviews, and maintain clear documentation for training-ready datasets. Strong judgment to escalate guideline gaps and propose rubric improvements that reduce rater variance.

Compensation

Salary range: 63360 to 126720 USD per year (FULL_TIME). Final compensation depends on evaluation scope, domain complexity (NLP, computer vision, content safety), and demonstrated QA leadership in production labeling workflows.

How to Apply

Apply through Rex.zone to be considered for Senior Data Labeling jobs aligned with Warsaw search intent and remote-first delivery. Your application should highlight RLHF evaluation, QA evaluation methods, annotation guidelines compliance, and examples of model performance improvement driven by better training data quality.

Frequently Asked Questions

  • Q: Is this role remote even though the keyword includes Warsaw?

    Yes. The Remote Type remains Remote, and the role is designed for remote execution while matching Warsaw-based search intent for senior data labeling jobs.

  • Q: What does “Senior Data Labeling” mean in practice?

    It typically includes ownership of annotation guidelines compliance, QA evaluation strategy, rater calibration, and higher-complexity tasks like RLHF preference ranking, prompt evaluation, and content safety labeling that directly influence large language model evaluation outcomes.

  • Q: Which domains are covered: NLP, computer vision, or both?

    Both. Workstreams may include named entity recognition and text classification (NLP) as well as computer vision annotation such as boxes, polygons, and segmentation, depending on project needs.

  • Q: What is RLHF work in data labeling?

    RLHF (Reinforcement Learning from Human Feedback) commonly involves rating or ranking model outputs, applying rubrics to evaluate helpfulness and safety, and producing preference signals that improve model behavior in LLM training pipelines.

  • Q: How is quality measured?

    Quality is measured through QA evaluation methods such as audit sampling, gold set accuracy, inter-annotator agreement, error taxonomies, and adherence to annotation guidelines, with feedback loops tied to model performance improvement.

  • Q: Is this a full-time role and what is the salary range?

    Yes, Employment Type is FULL_TIME. The salary range is 63360 to 126720 USD per year, with final compensation dependent on scope and demonstrated senior-level QA and evaluation capability.

230+Domains Covered
120K+PhD, Specialist, Experts Onboarded
50+Countries Represented

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