Senior Manager of Quality Assurance, Aiml Data Operations

Apple Apple · Big Tech · Cupertino, CA · Software and Services

Senior Manager of Quality Assurance for AIML Data Operations at Apple. This role focuses on leading the QA function for data annotation pipelines that feed into AI/ML models. Responsibilities include defining quality standards, managing a team of QA professionals, developing scalable QA processes, and collaborating with Data Science and ML Engineering teams to ensure high-quality labeled data. The role requires a strong understanding of AIML concepts and practical experience in data quality assurance at scale.

What you'd actually do

  1. Define, own, and continuously improve QA standards, frameworks, and metrics for data annotation tasks across multiple data types (text, audio, image, video, and multimodal).
  2. Develop and implement scalable QA protocols — including sampling strategies, inter-annotator agreement measures, and error taxonomy frameworks — to ensure consistent, high-quality labeled data.
  3. Lead root cause analysis and post-incident reviews for quality failures; drive systematic process improvements to prevent recurrence.
  4. Lead, coach, and grow a team of QA Specialists, QA Leads, and Program Coordinators — setting clear goals, providing ongoing feedback, and supporting career development.
  5. Partner with Data Science and ML Engineering teams to understand model requirements, translate them into annotation quality standards, and close feedback loops efficiently.

Skills

Required

  • Bachelor's degree in a relevant field (Computer Science, Linguistics, Data Science, Operations, or equivalent)
  • 8+ years of experience in quality assurance, data operations, or a related field
  • 10+ years of people management experience leading QA or data operations teams
  • Demonstrated experience defining and operating QA programs for data annotation or content labeling at scale
  • Solid understanding of AIML concepts, with practical knowledge of how data quality affects model performance
  • Strong analytical skills with experience using data to measure, communicate, and drive quality improvements
  • Professional fluency in English; excellent written and verbal communication skills across all levels of an organization

Nice to have

  • Master's degree or advanced certification in a relevant discipline
  • Experience with annotation platforms and QA tooling (e.g., Label Studio, Scale AI, Surge, Toloka, or similar)
  • Familiarity with inter-annotator agreement methodologies (Cohen's Kappa, Krippendorff's Alpha, etc.)
  • Experience managing QA for multilingual or multimodal annotation datasets
  • Track record of building or scaling QA programs in a globally distributed, vendor-augmented operating model
  • Passion for Apple Intelligence products and a deep appreciation for the role of data quality in user experience
  • Experience with statistical sampling techniques and quality auditing frameworks
  • Familiarity with RLHF (Reinforcement Learning from Human Feedback) data workflows

What the JD emphasized

  • own the end-to-end quality assurance strategy for annotation pipelines
  • ensure that data quality is not an afterthought — it is a foundation
  • own the end-to-end quality assurance strategy
  • ensure that data quality is not an afterthought
  • own the end-to-end quality assurance strategy for annotation pipelines
  • ensure that data quality is not an afterthought — it is a foundation
  • own the end-to-end quality assurance strategy
  • ensure that data quality is not an afterthought
  • Define, own, and continuously improve QA standards
  • Develop and implement scalable QA protocols
  • Lead root cause analysis and post-incident reviews for quality failures
  • Advocate for and oversee the integration of automated quality checks
  • Establish and track QA KPIs and OKRs
  • Lead, coach, and grow a team of QA Specialists, QA Leads, and Program Coordinators
  • Foster an inclusive team culture grounded in curiosity, rigor, psychological safety, and a commitment to continuous improvement.
  • Conduct regular performance reviews, identify skill gaps, and partner with L&D to address development needs within the team.
  • Hire and onboard talent thoughtfully, contributing to a diverse and high-performing QA organization.
  • Partner with Data Science and ML Engineering teams to understand model requirements, translate them into annotation quality standards, and close feedback loops efficiently.
  • Collaborate with Annotation Program Managers and Vendor Operations to embed QA practices into vendor workflows and third-party annotation pipelines.
  • Work with the Director of Data Operations to align QA strategy with broader organizational priorities and resource planning.
  • Serve as the primary QA point of contact for cross-functional stakeholders — communicating clearly on quality status, risk, and mitigation strategies.
  • Drive change management efforts when introducing new QA tooling, processes, or standards across global teams.
  • Manage QA capacity planning to ensure sufficient coverage across global annotation programs of varying scale and complexity.
  • Identify opportunities to streamline QA workflows, reduce turnaround time, and improve cost-efficiency without compromising quality standards.
  • Stay current on industry best practices in data annotation quality, emerging AI evaluation methodologies, and tooling innovations.
  • 8+ years of experience in quality assurance, data operations, or a related field
  • 10+ years of people management experience leading QA or data operations teams
  • Demonstrated experience defining and operating QA programs for data annotation or content labeling at scale
  • Solid understanding of AIML concepts, with practical knowledge of how data quality affects model performance
  • Strong analytical skills with experience using data to measure, communicate, and drive quality improvements
  • Professional fluency in English; excellent written and verbal communication skills across all levels of an organization
  • Ability to travel internationally when required
  • Experience with annotation platforms and QA tooling (e.g., Label Studio, Scale AI, Surge, Toloka, or similar)
  • Familiarity with inter-annotator agreement methodologies (Cohen's Kappa, Krippendorff's Alpha, etc.)
  • Experience managing QA for multilingual or multimodal annotation datasets
  • Track record of building or scaling QA programs in a globally distributed, vendor-augmented operating model
  • Passion for Apple Intelligence products and a deep appreciation for the role of data quality in user experience
  • Experience with statistical sampling techniques and quality auditing frameworks
  • Familiarity with RLHF (Reinforcement Learning from Human Feedback) data workflows

Other signals

  • leading QA for data annotation pipelines
  • defining and defending data quality standards
  • managing a team of QA professionals
  • ensuring data quality for AI/ML models
  • implementing scalable QA processes