Must Have Technical/Functional Skills
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.
Generic Managerial Skills, If Any
Strong attention to detail, analytical thinking, process discipline, stakeholder communication, and ability to influence quality improvements across teams.