QA Lead/QA Exec

Job Responsibilities:

• Lead and manage a team of QAs within the assigned project domain (Audio / Video / LLM), ensuring quality objectives and SLAs are consistently met.
  
• Develop and standardize QA rubrics, error typologies, and review guidelines based on project-specific annotation requirements.
  
• Conduct calibration sessions regularly with QAs, Trainers, and PMs to maintain consistent quality interpretations across teams.
  
• Analyze QA reports and annotation error trends to identify systemic issues and recommend corrective actions.
  
• Collaborate with Trainers to translate recurring quality gaps into targeted training or retraining plans.
  
• Perform quality audits on both QA and annotator performance to ensure review accuracy and reliability.
  
• Monitor and report key quality metrics (Accuracy, Consistency, Disagreement Rate, Rejection Rate, etc.) to stakeholders.
  
• Design and optimize QA sampling strategies to ensure efficient yet effective quality coverage.
  
• Support new project launches by developing quality validation processes, test datasets, and pilot evaluation rubrics.
  
• Drive continuous improvement initiatives through process standardization, tool optimization, and cross-domain knowledge sharing.
  
• Ensure data integrity and confidentiality in line with client and internal security requirements.
  

Job Requirements:

• Bachelors degree in Linguistics, Data Science, Computer Science, Engineering, or a related field.
• 3~5 years of experience in data labeling, quality assurance, or content review (preferably in AI data operations).
• Proven track record in managing QA teams and driving performance improvements.
• Strong analytical and problem-solving skills; able to interpret large sets of QA and error data.
• Excellent communication and collaboration skills; able to align QA, training, and delivery stakeholders.
• Familiarity with annotation tools, QA platforms, and performance tracking dashboards (e.g., Airtable, Smartsheet, Jira, Labelbox, etc.).

Preferred:

• Prior experience in Audio, Video, or LLM annotation quality management.
• Knowledge of process improvement methodologies (Six Sigma, Kaizen, or equivalent).
• Experience designing QA rubrics and calibration frameworks.
• Background in AI data services, MLOps, or data quality governance.