About Welo Data
Welo Data, a Welo Global brand, is the multilingual data and evaluation partner for foundation labs and enterprises deploying GenAI systems globally. They deliver the human judgment, data infrastructure, and evaluation systems that ensure AI models perform reliably across languages, cultures, and real-world contexts, at every stage from training through deployment. Its global network of 500,000+ vetted experts spans 300+ languages and locales, enabling high-quality multilingual data creation and structured model evaluation across the full spectrum of modern AI applications — from large language models and voice and speech systems to agentic workflows and robotics and embodied AI. This breadth of linguistic, cultural, and domain expertise enables Welo Data to address critical AI development challenges, including safety, bias, inclusivity, and cross-lingual reliability. A unified global operating model, led by specialized program and quality experts and grounded in assessment-driven talent selection, localized rubrics, and continuous calibration, ensures consistent performance across languages, domains, and modalities. Underpinning all of this is NIMO™ (Network Identity Management and Operations), Welo Data's proprietary identity and fraud-prevention framework. Built to maintain data integrity and workforce trust across a global contributor base, NIMO combines advanced verification, continuous monitoring, and structured QA to ensure every dataset is accurate, traceable, and culturally grounded. welodata.ai
Role Overview
The Quality Control Coordinator for AI Data Annotation is responsible for supporting quality assurance processes to ensure annotated datasets meet defined guidelines and client standards. This role focuses on sampling, defect tracking, training coordination, documentation management, and audit readiness.
The position plays a critical role in maintaining high-quality datasets used for training and evaluating machine learning models, working closely with operations, project managers, and annotation teams.
Main Responsibilities
- Perform sampling and quality checks on annotated datasets (text, image, audio, 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 refresher training for annotators and reviewers
- Maintain and update annotation guidelines, SOPs, and rubrics, ensuring proper version control and communication
- Prepare client-ready quality reports, including tables, charts, and summaries, with consistent formatting
- Liaise with Project Managers and Operations teams to align timelines, reporting, and audit requirements
- Manage access permissions for annotation tools, QA platforms, and shared repositories
- Support vendor coordination, including documentation requests, SLAs, and quality expectations
- Identify process gaps and recommend practical improvements (e.g., templates, QA checklists, sampling approaches)
Skills and Qualifications
- Bachelor’s degree in any discipline (Data Science, Computer Science, Linguistics, or related fields preferred)
- Strong understanding of annotation workflows (bounding boxes, segmentation, classification, transcription, etc.)
- Familiarity with QA metrics such as accuracy, precision/recall, F1 score, and inter-annotator agreement
- Proficiency in MS Excel or Google Sheets (pivot tables, dashboards, data analysis)
- Strong attention to detail and ability to identify subtle quality issues in datasets
- Excellent communication and coordination skills, with the ability to work across cross-functional teams
- Strong organizational skills and ability to manage multiple priorities in a fast-paced environment
Ideal Background and Experience
- 2–4 years of experience in Quality Control/Quality Assurance within AI data annotation, data labeling, or content moderation
- Experience working with annotation tools (e.g., Labelbox, CVAT, Scale AI, or similar platforms)
- Exposure to NLP, Computer Vision, or Speech datasets
- Basic understanding of machine learning workflows and data lifecycle
- Experience working with global clients and distributed or remote annotation teams
This freelance 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.
