QC Coordinator (Europe/America based)

Overview

QC Coordinator (Europe/America based) Jobs in Greater Madrid Metropolitan Area at Welocalize

Title: QC Coordinator (Europe/America based)

Company: Welocalize

Location: Greater Madrid Metropolitan Area

Job Title: Quality Control (QC) Coordinator
Locaton- Remote  (Europe/America based)
Type- Feelance

 

Role Overview
The QC Coordinator for AI Data Annotation supports quality assurance processes to ensure annotated datasets meet defined guidelines and client standards.The role focuses on sampling, defect tracking, training coordination, documentation management, and audit readiness.This position plays a key role in maintaining data quality critical for training and evaluating machine learning models.

 

Key Responsibilities 

• Perform sampling and quality checks on annotated datasets (text, image, audio, or video) to ensure adherence to annotation guidelines
• Identify, log, and categorize annotation defects (e.g., labeling errors, boundary issues, misclassification) with severity levels
• Track corrective actions and rework tasks to closure; validate re-tests and document outcomes
• Coordinate onboarding training, calibration sessions, and periodic refreshers for annotators and reviewers
• Ensure annotation guidelines, SOPs, and rubrics are updated, version-controlled, and clearly communicated to stakeholders
• Identify process gaps and recommend practical improvements (annotation templates, QA checklists, sampling strategies). Manage access permissions for annotation tools, QA platforms, and shared repositories

Required Skills and Qualifications

• Bachelor degree in any discipline (Data Science, Computer Science, Linguistics, or related fields preferred)
• 2-4 years of experience in Quality Control/Quality Assurance within AI data annotation, data labeling, or content moderation
• Strong understanding of annotation workflows (bounding boxes, segmentation, classification, transcription, etc.)
• Familiarity with QA metrics such as accuracy, F1 score, precision/recall, and inter-annotator agreement
• Proficiency in MS Excel/Google Sheets (pivot tables, dashboards, data analysis)
• Strong communication and coordination skills across cross-functional teams

Preferred Qualifications

• Experience with annotation tools (e.g., Labelbox, CVAT, Scale AI, or similar platforms)
• Exposure to NLP, Computer Vision, or Speech datasets
• Basic knowledge of machine learning workflows and data lifecycle
• Experience working with global clients and remote annotation teams

Primary Responsibility

• Performing sampling and guideline-adherence checks; logging defects with categorization and severity
• Tracking corrective actions to closure; verifying re-tests and documenting outcomes
• Coordinating training sessions and refreshers; managing attendance and quick assessments
• Keeping guidelines, SOPs, and rubrics current and version-controlled; managing release notes
• Preparing client-ready exports (tables, charts) with consistent formatting and footnotes
• Liaising with PMs and Ops to align timelines and inputs for reporting and audits
• Managing access requests and permissions for QA tools and folders
• Supporting vendor coordination (checklists, SLAs, documentation requests)
• Identifying minor process gaps and suggesting simple fixes (templates, checklists)

Ideal Profile

• 2-4 years of experience in Quality Control/Quality Assurance within AI data annotation, data labeling, or content moderation
• Strong understanding of annotation workflows (bounding boxes, segmentation, classification, transcription, etc.)
• Proficiency in MS Excel/Google Sheets (pivot tables, dashboards, data analysis)
• Familiarity with QA metrics such as accuracy, F1 score, precision/recall, and inter-annotator agreement
• Excellent attention to detail and ability to identify subtle quality issues in datasets
• Strong communication and coordination skills across cross-functional teams

This role offers an exciting opportunity to contribute to cutting-edge AI projects while building expertise in data quality and annotation processes. Join us to play a key role in shaping high-quality datasets that power intelligent systems.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.