[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"job-remote-data-annotation-jobs-helsinki":3},{"Ques":4,"Slug":31,"Header":32,"job_category":62},{"title":5,"content":6},"Frequently Asked Questions",[7,10,13,16,19,22,25,28],{"A":8,"Q":9},"Yes. Remote Type is Remote, and all workflows are designed for asynchronous collaboration, online tooling, and remote QA review.","Are these remote data annotation jobs in Helsinki fully remote?",{"A":11,"Q":12},"Work may include data labeling for NLP and computer vision annotation, named entity recognition, content safety labeling, prompt evaluation, and RLHF preference ranking with QA evaluation.","What kind of annotation work is included?",{"A":14,"Q":15},"This posting is for FULL_TIME employment. Rex.zone may also list contract or freelance roles separately depending on project needs.","Is this role full-time or contract?",{"A":17,"Q":18},"Experience Level is Mid-Senior. You should be comfortable applying detailed rubrics, handling edge cases, and maintaining high training data quality under QA review.","What experience level is required?",{"A":20,"Q":21},"RLHF tasks involve comparing model responses, ranking preferences, and scoring outputs against a rubric to improve large language model evaluation and downstream tuning outcomes.","How does RLHF relate to this job?",{"A":23,"Q":24},"Core skills include data annotation, data labeling, RLHF, prompt evaluation, QA evaluation, named entity recognition, computer vision annotation, content safety labeling, and familiarity with LLM training pipelines.","What skills are most important for success?",{"A":26,"Q":27},"Quality is managed through calibration sets, audits, reviewer feedback, and annotation guidelines compliance checks, focusing on consistency and model performance improvement outcomes.","How is quality managed?",{"A":29,"Q":30},"Apply via Rex.zone and follow the screening and calibration steps to be matched to active remote production projects.","Where do I apply?","remote-data-annotation-jobs-helsinki",{"desc":33,"title":34,"content":35},"Rex.zone is hiring for remote data annotation jobs in Helsinki focused on AI training data for large language models and computer vision systems. As a data annotation specialist, you will perform data labeling, RLHF evaluation, prompt evaluation, and QA evaluation to improve training data quality, annotation guidelines compliance, and model performance improvement across LLM training pipelines. This role supports workflows used by AI labs, tech startups, annotation vendors, and BPO teams, with opportunities across NLP, named entity recognition, content safety labeling, and image\u002Fvideo annotation. Apply on Rex.zone to join full-time remote projects with clear quality metrics and production-grade review processes.","Remote Data Annotation Jobs in Helsinki",[36,38,41,44,47,50,53,56,59],{"h2":34,"desc":37},"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 Annotation, Data Labeling, RLHF, Prompt Evaluation, QA 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",{"h2":39,"desc":40},"About the Role","You will annotate and evaluate multi-modal datasets (text, image, and structured records) used to train and validate AI systems. Your work will include data labeling, RLHF preference ranking, prompt-response evaluation, and QA evaluation against detailed annotation guidelines. You will collaborate asynchronously with project leads to resolve edge cases, calibrate rubric interpretation, and ensure training data quality for downstream model performance improvement.",{"h2":42,"desc":43},"Key Responsibilities","You will execute labeling tasks across NLP and computer vision annotation, including named entity recognition, classification, summarization checks, and content safety labeling. You will perform RLHF evaluations such as pairwise ranking and rubric-based scoring, document rationales, and flag ambiguous prompts. You will run quality assurance passes, follow annotation guidelines compliance requirements, and help refine taxonomy definitions to reduce disagreement and improve inter-annotator consistency.",{"h2":45,"desc":46},"Projects You May Support","LLM training pipelines: prompt evaluation, instruction-following checks, and helpfulness\u002Fharmlessness assessments. NLP data labeling: named entity recognition, intent classification, and information extraction. Computer vision annotation: bounding boxes, polygons, keypoints, and image categorization. Content safety labeling: policy-based classification for sensitive, unsafe, or restricted content with escalation paths.",{"h2":48,"desc":49},"Required Qualifications","Mid-to-senior experience in data annotation or data labeling operations with measurable quality outcomes. Ability to apply detailed rubrics consistently, write clear rationales for QA evaluation, and resolve edge cases. Familiarity with RLHF concepts, prompt evaluation workflows, and large language model evaluation signals. Strong English comprehension and professional writing for guideline interpretation and issue reporting.",{"h2":51,"desc":52},"Preferred Qualifications","Experience with named entity recognition (NER) schemas, content safety labeling policies, or computer vision annotation tools. Exposure to inter-annotator agreement practices, gold set calibration, and audit workflows. Comfort working in remote, asynchronous production environments with ticketing systems, review queues, and quality metrics.",{"h2":54,"desc":55},"How Work Is Measured","Quality is measured using annotation guidelines compliance, audit pass rate, calibration accuracy, and consistency across repeated items. Productivity targets are set per project type and balanced with accuracy. You will receive feedback through QA evaluation, targeted retraining tasks, and periodic calibration sessions to support model performance improvement.",{"h2":57,"desc":58},"Why Rex.zone","Rex.zone connects remote annotators with production AI\u002FML teams, including AI labs, tech startups, annotation vendors, and BPO programs. You will work on real-world AI training data that powers evaluation, safety, and capability improvements for deployed systems. Projects span remote, full-time programs with structured QA, clear rubrics, and scalable workflows.",{"h2":60,"desc":61},"How to Apply","Apply through Rex.zone and complete the role-specific qualification flow. Be prepared for a short calibration task covering data labeling accuracy, prompt evaluation judgment, and annotation guidelines compliance. If selected, you will be onboarded to a project queue and assigned QA evaluation checkpoints during the ramp-up period.","AI Data Operations"]