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.