Remote Data Labeling Jobs in Warsaw

Remote data labeling jobs in Warsaw at Rex.zone focus on training data creation and evaluation for AI/ML systems, including LLM training pipelines, RLHF, prompt evaluation, and quality assurance. You will label text, images, and audio; follow annotation guidelines compliance; and perform training data quality checks that support model performance improvement for NLP, computer vision, and content safety workflows. This page helps you explore and apply to full-time remote roles that match modern annotation vendors, AI labs, tech startups, and BPO delivery teams working on large language model evaluation and dataset operations.

Remote Data Labeling Jobs in Warsaw — LinkedIn Job Metadata

About the Role

You will deliver high-quality labeled datasets and human evaluations used to train and validate machine learning models. Work includes text annotation for NLP and named entity recognition, ranking and preference tasks for RLHF, prompt evaluation for large language model evaluation, and image/video labeling for computer vision annotation (bounding boxes, polygons, keypoints). You will also support QA evaluation through audits, disagreement resolution, and rubric-driven reviews to improve training data quality and downstream model performance.

What You Will Do

Execute data labeling tasks across modalities (text, image, audio) using project-specific taxonomies; perform RLHF preference ranking and rationale capture aligned to evaluation rubrics; complete prompt evaluation and response grading for LLM training pipelines; follow annotation guidelines compliance and document edge cases; run QA evaluation checks (spot checks, gold set validation, inter-annotator agreement review); flag policy, privacy, and content safety labeling issues; collaborate asynchronously with leads to iterate on instructions and reduce ambiguity; produce clear task notes that improve dataset consistency and model performance improvement.

Key Workflows and Domains

NLP: classification, named entity recognition, sentiment, summarization evaluation, prompt evaluation; Computer Vision: computer vision annotation with bounding boxes, polygons, segmentation masks, keypoints, attribute tagging; Content Safety: policy labeling, toxicity and sensitive content identification, red teaming style evaluation; LLM Evaluation: rubric-based scoring, pairwise ranking for RLHF, hallucination and factuality checks, instruction-following assessment; QA: audit sampling, error taxonomy creation, guideline refinement, consensus building.

Requirements

Mid-Senior experience delivering data annotation or evaluation work with measurable quality targets; strong attention to detail and comfort interpreting annotation guidelines; ability to reason about ambiguous language and apply consistent labels; experience with QA evaluation concepts (gold data, audits, disagreement analysis); familiarity with NLP concepts (entities, intent, classification) and/or computer vision annotation formats; ability to work full-time remote with reliable connectivity and secure work practices.

Preferred Qualifications

Hands-on experience with RLHF tasks (pairwise ranking, preference modeling support) and prompt evaluation; experience with training data quality programs and annotation guidelines compliance frameworks; familiarity with content safety labeling, policy taxonomies, and escalation workflows; experience supporting large language model evaluation projects and model performance improvement efforts; comfort using common annotation tools and producing clear documentation for edge cases.

Quality and Performance Expectations

Maintain consistent labeling aligned with rubrics and annotation guidelines compliance; hit throughput goals without sacrificing training data quality; achieve target audit pass rates and contribute to QA evaluation improvements; communicate uncertainty early and propose guideline clarifications; protect sensitive data and follow privacy and security requirements throughout LLM training pipelines.

Compensation and Employment Details

Salary range is USD 63360 to 126720 per year for FULL_TIME employment, dependent on skills alignment, scope, and evaluation complexity. Remote Type remains Remote. This role supports multiple client domains including NLP, computer vision annotation, content safety labeling, and large language model evaluation.

How to Apply on Rex.zone

Apply through Rex.zone by submitting your profile, highlighting experience in data labeling, RLHF, prompt evaluation, QA evaluation, and training data quality. Include examples of annotation guidelines compliance work, domain familiarity (NLP, computer vision, content safety), and any experience improving model performance improvement via better datasets.

Frequently Asked Questions

Q: What are remote data labeling jobs in Warsaw?
They are remote roles where you create and validate labeled datasets from Warsaw or for Warsaw-based candidates, supporting AI/ML development through data labeling, RLHF, prompt evaluation, and QA evaluation for NLP, computer vision annotation, and content safety labeling.
Q: Is this role fully remote and full-time?
Yes. Remote Type is Remote and Employment Type is FULL_TIME, with responsibilities designed for asynchronous delivery and quality-controlled workflows.
Q: What kinds of tasks will I do day to day?
Common tasks include text annotation (classification and named entity recognition), image/video labeling for computer vision annotation, rubric-based large language model evaluation, RLHF pairwise ranking, prompt evaluation, and QA evaluation audits to maintain training data quality.
Q: Do I need prior RLHF or LLM evaluation experience?
It is preferred but not always required. Strong annotation guidelines compliance, attention to detail, and QA evaluation discipline are critical; RLHF and prompt evaluation experience helps you ramp faster on LLM training pipelines.
Q: What skills should I highlight for these remote data labeling jobs in Warsaw?
Highlight data labeling, data annotation, RLHF, prompt evaluation, QA evaluation, training data quality, annotation guidelines compliance, named entity recognition, computer vision annotation, content safety labeling, and familiarity with LLM training pipelines.
Q: What types of employers use this kind of work?
Projects can support AI labs, tech startups, annotation vendors, and BPO delivery teams building datasets for NLP, computer vision, and content safety, as well as large language model evaluation and RLHF programs.
Q: Are contract or freelance options available too?
This posting is FULL_TIME, but many data labeling teams also run contract and freelance programs depending on client demand and project timelines.
Q: How does QA work in data labeling?
QA evaluation typically includes gold set validation, sampling audits, disagreement analysis, and edge-case documentation to enforce annotation guidelines compliance and improve training data quality for better model performance improvement.

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