AI Data Collection Lead — Annotation & Quality

Quality Ai; Hinoba-an; On-site; PHP 396,000 - 594,000; Full time

Job summary

QualityAI is seeking a Data Collection & Annotation Team Lead to guide a five-member AI data team in collecting, annotating, and validating image and video datasets for model training. You will plan daily tasks, allocate priorities, monitor quality, and ensure on-time delivery while hands-on data collection when needed.

Lead by example, set annotation guidelines, and coordinate with engineering, QA, and validation teams to resolve data-quality issues.

Qualifications

• 1–3 years of experience in Data Annotation, Data Collection, Validation, or similar technical role.
• Experience leading a small team or acting as a technical focal point is advantageous.
• Strong organizational skills and attention to detail.

Responsibilities

• Lead and manage a team of five AI Data Collection & Annotation Engineers.
• Plan and assign daily data collection and annotation activities according to project priorities.
• Monitor progress, productivity, quality, and delivery timelines.
• Ensure data collection scenarios meet predefined requirements and test conditions.
• Ensure annotation guidelines are followed and datasets are reviewed for accuracy.
• Provide ongoing training and onboarding for new team members.
• Hands-on work with camera systems and dedicated setups for data collection in varied environments.
• Identify issues and escalate data-quality concerns to stakeholders.
• Coordinate with AI, QA, Validation, and Engineering teams.

Skills

data annotation

data collection

team leadership

communication

Education

Bachelor's degree

Tools

CVAT

labeling tools

Job description

QualityAI is seeking a Data Collection & Annotation Team Lead to guide a five-member AI data team in collecting, annotating, and validating image and video datasets for model training. You will plan daily tasks, allocate priorities, monitor quality, and ensure on-time delivery while hands-on data collection when needed.

Lead by example, set annotation guidelines, and coordinate with engineering, QA, and validation teams to resolve data-quality issues.