Remote AI Data Annotation Specialist

HUMAN-AI-Z Nexus Technology; Dubai; On-site; AED 147,000 - 220,000; Full time

Job summary

HUMAN-AI-Z Nexus Technology is seeking a Data Specialist for a remote, full-time role. You will manage high-quality datasets used in AI model training and evaluation, ensuring accurate labeling and data integrity across pipelines.

As a New Grad entry-level opportunity, you will refine annotation guidelines, monitor data quality, and collaborate with cross-functional teams to support reliable machine learning workflows in a global technology environment.

Qualifications

• Experience with data annotation, labeling, or quality assurance processes is preferred.
• Familiarity with basic data validation techniques and attention to detail in reviewing datasets.
• Ability to follow structured guidelines and maintain consistency in data handling tasks.
• Basic understanding of AI/ML concepts and their dependence on high-quality training data is beneficial.
• Proficiency in using spreadsheets or data management tools to track and organize information.

Responsibilities

• Review and validate human-labeled data to ensure consistency and accuracy for AI training datasets
• Identify and resolve discrepancies in data annotations by cross-referencing source materials and guidelines
• Collaborate with cross-functional teams to refine annotation guidelines and improve data quality standards
• Monitor and report on data labeling performance metrics to track progress and identify trends
• Assist in documenting processes and maintaining records of data sources, labeling rules, and quality checks

Skills

Data annotation

Quality assurance

Attention to detail

AI/ML concepts

Data management tools

Job description

HUMAN-AI-Z Nexus Technology is seeking a Data Specialist for a remote, full-time role. You will manage high-quality datasets used in AI model training and evaluation, ensuring accurate labeling and data integrity across pipelines.

As a New Grad entry-level opportunity, you will refine annotation guidelines, monitor data quality, and collaborate with cross-functional teams to support reliable machine learning workflows in a global technology environment.