Apple-posted 2 months ago
Manager
Cupertino, CA
5,001-10,000 employees
Computer and Electronic Product Manufacturing

We are seeking a Quality Engineering Manager to lead and guide a team focused on ensuring the quality of data used for training and evaluating machine learning models. This role is critical in shaping the processes, tools, and standards that underpin high-quality training data. It combines data analysis, technical mentorship, and operational execution to strengthen our quality assurance systems and drive impact at scale. The ideal candidate is comfortable navigating complexity, collaborating multi-functionally, and making data-driven decisions that directly influence model performance.

  • Manage and support a team of Quality Engineers responsible for QA strategy, tooling, and implementation across annotation workflows.
  • Define and refine scalable processes and metrics to assess the quality, consistency, and relevance of labeled data.
  • Partner with Engineering teams to build automated validation logic for detecting inconsistencies and data anomalies.
  • Collaborate with Data Scientists and ML Engineers to analyze how data quality impacts model behavior and identify opportunities for data improvement.
  • Lead multi-functional alignment on annotation QA standards and ensure feedback loops between quality, guidelines, and tooling.
  • Own and evolve golden set evaluations, consensus grading protocols, and annotator quality tracking mechanisms.
  • Conduct root cause analyses on quality issues and drive corrective actions in collaboration with upstream teams.
  • Stay ahead of QA and data quality best practices, and drive continuous improvement in tools and methodologies.
  • Experience with AI/ML/LLM.
  • Familiarity with ML model development cycles and the role of human-labeled data in training and evaluation.
  • Experience with large-scale data operations or data-centric ML infrastructure.
  • Familiarity with human-in-the-loop evaluation, data quality frameworks, and annotation tooling platforms.
  • Knowledge of data quality standards, frameworks, and governance best practices.
  • Experience designing and implementing automated QA checks and quality monitoring systems.
  • Strong data analysis skills; experience with scripting languages (e.g., Python) and data tools (e.g., SQL).
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