Senior Manager of Quality Assurance, AIML Data Operations

Senior Manager of Quality Assurance, AIML Data Operations

Cupertino, California, United States Software and Services

Imagine what you could do here. At Apple, great ideas have a way of becoming great products, services, and customer experiences — quickly.

The AIML team is looking for a passionate, detail-obsessed Senior Manager of Quality Assurance to lead the QA function within our Data Annotation operations. This is a rare opportunity to directly shape the quality standards that underpin the intelligent systems used by hundreds of millions of people every day.

You will lead a team of QA professionals, define and defend data quality standards, and champion a culture of rigorous, scalable quality assurance across global annotation workflows. If you thrive at the intersection of operational excellence, data quality, and cross-functional leadership — this role was built for you.

As Senior Manager of QA for Data Annotation, you will own the end-to-end quality assurance strategy for annotation pipelines that feed directly into Apple's AI and machine learning models. You will partner closely with Data Science, Engineering, and Operations leadership to ensure that data quality is not an afterthought — it is a foundation.

You will manage and develop a team of QA specialists and leads, set clear quality metrics, and build scalable processes that grow with our annotation programs. Your decisions will have measurable, real-world impact on the performance of Apple Intelligence products.

• Quality Assurance Strategy & Execution
• Define, own, and continuously improve QA standards, frameworks, and metrics for data annotation tasks across multiple data types (text, audio, image, video, and multimodal).
• Develop and implement scalable QA protocols — including sampling strategies, inter-annotator agreement measures, and error taxonomy frameworks — to ensure consistent, high-quality labeled data.
• Lead root cause analysis and post-incident reviews for quality failures; drive systematic process improvements to prevent recurrence.
• Advocate for and oversee the integration of automated quality checks into annotation pipelines to increase throughput without sacrificing accuracy.
• Establish and track QA KPIs and OKRs; provide regular data-driven reporting to senior and executive leadership on quality performance and trends.
• Lead, coach, and grow a team of QA Specialists, QA Leads, and Program Coordinators — setting clear goals, providing ongoing feedback, and supporting career development.
• 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.
• Cross-Functional & Stakeholder Collaboration
• 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.
• Operational Excellence
• 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.

• Bachelor's degree in a relevant field (Computer Science, Linguistics, Data Science, Operations, or…