Description

• Demonstrated experience in annotation quality, machine learning data operations, quality engineering, or data quality supporting physical AI, robotics, computer vision, or machine learning programs.

• Demonstrated experience developing quality metrics and performance measurement systems.

• Demonstrated experience working with large-scale annotation programs.

• Demonstrated experience evaluating generated captions, reasoning outputs, or foundation model training datasets.

• Experience defining annotation instructions, sampling plans, acceptance criteria, defect taxonomies, and audit processes.

• Ability to analyze quality trends, identify root causes, and coordinate corrective actions.

• Working knowledge of SQL, Python, reporting, or data analysis tools used for quality measurement.

Roles & Responsibilities

• Define and maintain annotation quality standards, guidelines, defect categories, and acceptance criteria.

• Develop metrics and reporting for accuracy, consistency, completeness, rework, throughput, and reviewer agreement.

• Design sampling, audit, escalation, and root-cause analysis processes for large-scale annotation programs.

• Evaluate generated captions, reasoning outputs, and foundation model training datasets against defined quality criteria.

• Analyze quality trends and partner with annotation operations, engineering, and AI teams on corrective actions.

• Validate dataset releases and maintain traceable quality records, issue logs, and improvement plans.

• Support calibration sessions, reviewer alignment, and continuous improvement of annotation workflows

Key Skills

Education

Bachelor's degree