Senior Data Labeling Jobs in Stockholm (Remote)

Rex.zone is hiring for senior data labeling jobs supporting AI/ML training workflows, including RLHF, LLM evaluation, computer vision annotation, and QA evaluation. In this remote full-time role, you will apply annotation guidelines compliance, improve training data quality, and help drive model performance improvement through structured labeling, prompt evaluation, and content safety labeling. You will collaborate with engineers and AI teams to deliver high-accuracy datasets for named entity recognition, instruction tuning, and large language model evaluation while meeting throughput, accuracy, and audit requirements on Rex.zone.

Job Image

Job Heading: Senior Data Labeling Specialist (Stockholm, Remote)

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

About the Role

You will lead and execute senior-level data labeling work across NLP and computer vision projects, including RLHF ranking, preference labeling, prompt evaluation, and content policy enforcement. You will translate ambiguous model behaviors into clear labeling decisions, maintain high training data quality, and ensure annotation guidelines compliance across tasks such as named entity recognition, text classification, summarization evaluation, and image/video annotation.

What You Will Do

["Perform complex data labeling and data annotation for LLM training pipelines, including RLHF preference data and QA evaluation","Run prompt evaluation and large language model evaluation using rubrics, scoring scales, and pairwise comparisons","Create, refine, and enforce annotation guidelines; resolve edge cases and document decisions for auditability","Execute NLP annotation tasks such as named entity recognition, intent classification, sentiment, toxicity, and factuality checks","Execute computer vision annotation tasks such as bounding boxes, polygons, keypoints, segmentation masks, and attribute labeling","Perform content safety labeling (policy, violence, self-harm, hate, sexual content) with consistent taxonomy and escalation pathways","Conduct quality assurance: inter-annotator agreement checks, error analysis, sampling plans, and corrective feedback","Partner with engineering and operations to improve tooling, reduce label noise, and support model performance improvement"]

Required Qualifications

["Mid-Senior experience in data labeling, data annotation, or QA evaluation for AI/ML datasets","Hands-on familiarity with RLHF workflows, prompt evaluation, and LLM evaluation rubrics","Strong understanding of annotation guidelines compliance and how to handle ambiguous edge cases","Experience with training data quality methods: sampling, audits, adjudication, and error taxonomy","Comfort working in remote, metric-driven production environments (accuracy, throughput, consistency)"]

Preferred Qualifications

["Experience supporting NLP datasets (named entity recognition, classification, retrieval relevance, instruction following)","Experience supporting computer vision annotation (segmentation, keypoints, multi-object tracking)","Experience with content safety labeling and policy-based decisioning","Ability to write clear guideline updates and train peers on rubric interpretation","Familiarity with dataset versioning, gold sets, and evaluation benchmarks"]

How Success Is Measured

["Training data quality: sustained accuracy on gold sets and reduced label noise","Annotation guidelines compliance: consistent decisions and documented edge-case handling","Model performance improvement: measurable lift signals from cleaner labels and better preference data","Operational excellence: on-time delivery, predictable throughput, and high audit readiness","Quality assurance impact: improved inter-annotator agreement and lower rework rates"]

Who This Role Is For

This role fits candidates searching for senior data labeling jobs in Stockholm who want remote full-time work on AI/ML data operations. You may have experience at AI labs, tech startups, annotation vendors, or BPO teams and want to focus on LLM training, RLHF evaluation, content safety labeling, and computer vision annotation with clear quality standards on Rex.zone.

Apply on Rex.zone

Explore and apply to this senior data labeling role on Rex.zone. Keep your resume focused on data labeling outcomes, training data quality methods, QA evaluation experience, and any RLHF or large language model evaluation work.

Frequently Asked Questions

  • Q: Is this a remote role even though the keyword targets Stockholm?

    Yes. The role is explicitly Remote. The Stockholm keyword reflects candidate search intent, while the work is performed remotely with distributed teams on Rex.zone.

  • Q: What does senior data labeling mean in AI/ML workflows?

    It typically means owning complex annotation decisions, improving annotation guidelines compliance, performing QA evaluation and adjudication, and producing high-quality RLHF and evaluation datasets that support LLM training pipelines and model performance improvement.

  • Q: What types of tasks are included?

    Common tasks include RLHF preference ranking, prompt evaluation, large language model evaluation, named entity recognition, text classification, computer vision annotation, and content safety labeling with structured rubrics.

  • Q: What tools will I use?

    You will use web-based labeling and review tools, QA sampling workflows, rubric-based evaluation forms, and dataset tracking systems used across Rex.zone projects.

  • Q: How is quality measured?

    Quality is measured using gold-set accuracy, inter-annotator agreement, audit pass rates, error analysis outcomes, and adherence to annotation guidelines compliance.

  • Q: Is this role full-time?

    Yes. Employment Type is FULL_TIME and Remote Type is Remote.

  • Q: Do I need machine learning engineering experience?

    No, but you should be comfortable working with AI/ML training workflows and evaluation concepts such as RLHF, prompt evaluation, and training data quality methods. The Job Function is Engineering because the work directly supports model training and evaluation pipelines.

  • Q: What industries hire for similar roles?

    AI labs, technology companies, tech startups, annotation vendors, and BPO providers commonly hire senior data labeling specialists to support NLP, computer vision, and content safety programs.

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

Industry-Leading Compensation

We believe exceptional intelligence deserves exceptional pay. Our platform consistently offers rates above the industry average, rewarding experts for their true value and real impact on frontier AI. Here, your expertise isn't just appreciated - it's properly compensated.

Work Remotely, Work Freely

No office. No commute. No constraints. Our fully remote workflow gives experts complete flexibility to work at their own pace, from any country, any time zone. You focus on meaningful tasks - we handle the rest.

Respect at the Core of Everything

AI trainers are the heart of our company. We treat every expert with trust, humanity, and genuine appreciation. From personalized support to transparent communication, we build long-term relationships rooted in respect and care.

Ready to Shape the Future of AI Data Operations?

Apply Now.