Senior Manager of Quality Assurance, AIML Data Operations

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. People Leadership & Team Development 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 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 Ability to travel internationally when required 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

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