Remote Data Annotation Jobs Amsterdam

Remote Data Annotation Jobs Amsterdam at Rex.zone connect experienced annotators with real-world AI/ML training workflows, including data labeling, RLHF evaluation, prompt evaluation, and training data quality improvements for large language models. You will apply annotation guidelines compliance, run QA evaluation, and help improve model performance across NLP, computer vision annotation, named entity recognition, and content safety labeling. These full-time remote roles support AI labs, tech startups, BPOs, and annotation vendors that rely on consistent, high-quality labeled datasets and human feedback loops for LLM training pipelines and model evaluation.

Job Heading: Remote Data Annotation Jobs Amsterdam

About the Role

As a mid-senior Data Annotation professional, you will deliver high-precision labels and human judgments used to train, fine-tune, and evaluate AI systems. Your work will include creating and applying consistent labeling taxonomies, validating edge cases, and improving training data quality through audits and peer review. Projects may span NLP classification, NER tagging, prompt evaluation for LLMs, RLHF preference ranking, computer vision annotation (bounding boxes, segmentation), and content safety labeling. You will collaborate asynchronously with data operations and model teams to align on annotation guidelines compliance and measurable quality targets.

Key Responsibilities

You will: (1) Annotate and review datasets for NLP, CV, and content safety tasks while following detailed rubrics; (2) Perform RLHF workflows such as preference ranking, pairwise comparisons, and justification capture for evaluator traceability; (3) Execute QA evaluation including sampling plans, discrepancy analysis, and rework loops to reduce label noise; (4) Document edge cases and propose guideline updates to improve inter-annotator agreement; (5) Support prompt evaluation and model evaluation sets that measure helpfulness, correctness, and safety; (6) Maintain secure handling of sensitive content and adhere to project confidentiality requirements.

Required Qualifications

You have: (1) 3+ years in data annotation, data labeling, QA evaluation, or model evaluation for AI/ML; (2) Demonstrated experience with annotation guidelines compliance and quality calibration methods; (3) Familiarity with RLHF, prompt evaluation, or LLM evaluation concepts; (4) Strong analytical writing for issue reports and edge-case documentation; (5) Comfort working remotely with distributed teams and structured throughput/quality metrics.

Preferred Qualifications

Nice to have: (1) Experience with named entity recognition, taxonomy design, or dataset curation; (2) Computer vision annotation experience including bounding boxes, polygons, or segmentation masks; (3) Experience with content safety labeling, policy-based decisions, and ambiguity handling; (4) Exposure to AI labs, tech startups, BPOs, or annotation vendors; (5) Familiarity with evaluation frameworks for large language model evaluation and model performance improvement.

Work Model, Tools, and Quality Standards

This is a Remote, FULL_TIME role with clear performance expectations tied to training data quality, annotation consistency, and audit outcomes. You will work in structured queues, follow project rubrics, and participate in calibration sessions to maintain inter-annotator agreement. Tooling varies by project and may include web-based labeling platforms, review consoles, and task-specific evaluators used for RLHF and prompt evaluation. You will be expected to track ambiguity, escalate guideline gaps, and contribute to continuous improvement of labeling operations.

Compensation

Salary Range: USD 63360 to 126720 per YEAR, depending on scope, domain complexity (NLP, computer vision, content safety), and evaluation responsibilities (RLHF, QA evaluation).

How to Apply on Rex.zone

Apply through Rex.zone to be considered for remote data annotation jobs aligned to Amsterdam searches and global remote teams. Keep your profile updated with labeling domains (NLP, CV, content safety), QA evaluation experience, and any RLHF or prompt evaluation work. Strong candidates include examples of guideline-driven decisions, quality audits, and measurable improvements to training data quality.

Frequently Asked Questions

Q: Are these roles truly remote even if I search from Amsterdam?
Yes. These positions are explicitly Remote. The Amsterdam keyword reflects search intent; assignments and teams are distributed, and work is completed remotely.
Q: What kind of data annotation work is included?
Common tasks include data labeling for NLP classification, named entity recognition, computer vision annotation, content safety labeling, prompt evaluation, RLHF preference ranking, and QA evaluation to improve training data quality.
Q: Is this full-time or contract/freelance?
This posting is FULL_TIME. Rex.zone may also list contract and freelance annotation roles, but this job page is for full-time remote hiring.
Q: What experience level is expected?
Mid-Senior. You should be comfortable interpreting complex rubrics, resolving edge cases, supporting QA evaluation, and contributing to guideline updates and model evaluation datasets.
Q: What skills should I highlight to match the job intent?
Emphasize data annotation, data labeling, RLHF, prompt evaluation, QA evaluation, annotation guidelines compliance, named entity recognition, computer vision annotation, content safety labeling, and experience supporting LLM training pipelines.
Q: What types of employers use Rex.zone for these roles?
Rex.zone supports hiring needs across AI labs, tech startups, BPOs, and annotation vendors building training datasets and evaluation pipelines for production AI systems.

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