Principal AI & Data Governance Architect

Summary

At Apple, great ideas have a way of becoming great products, services, and customer experiences very quickly. We are building the machine learning lifecycle platform that powers generative AI features across Apple Services — from Music and TV+ to iCloud, App Store, and Sports. As these systems grow in scale and complexity, building them responsibly is as important as building them well.

About the role

We are looking for a AI & Data Governance Architect who can define and drive the governance and compliance architecture for our AI and ML platform — holistically, not piecemeal. This is not a role about enforcing rules from the sidelines. It is about getting deep into how data flows, how models are built, how systems interconnect, and designing the platform capabilities that ensure Apple meets its legal, regulatory, and ethical obligations at every layer. You will be the person who understands both the compliance landscape and the engineering reality — and who builds the bridge between them.

In this role, you will

Architect end-to-end AI & Data governance frameworks — design the structures, policies, and technical controls that govern data classification, data lineage, model provenance, access management, and audit trails across the ML lifecycle — from raw data ingestion to model deployment and monitoring.

Own the compliance architecture — translate requirements from legal, privacy, and regulatory stakeholders into durable, scalable technical solutions. Understand not just what is required, but how it maps to systems, pipelines, and data assets — and design accordingly.

Connect compliance, legal, and engineering — serve as the authoritative bridge between legal counsel, privacy teams, data engineering, ML infrastructure, and ML researchers. Ensure that compliance requirements are understood in technical terms and that engineering decisions are grounded in regulatory reality.

Define data classification at scale — lead the strategy and architecture for classifying, cataloging, and governing the data assets that fuel Apple's AI and ML systems, including sensitive, regulated, and proprietary data across a diverse portfolio of use cases.

Navigate the regulatory landscape — have deep fluency in the legal and regulatory requirements relevant to AI/ML systems — data residency, retention, auditability, fairness, and emerging AI-specific regulation — and ensure our platform architecture is positioned to meet obligations as they evolve.

Establish governance as a cross-cutting platform capability — partner with data and AI & ML infrastructure teams to embed governance controls natively into the platform, making compliance the path of least resistance for ML researchers and engineers rather than a burden layered on top.

Drive policy into practice — develop the standards, playbooks, and architectural patterns that engineering teams across Apple Services can adopt consistently, ensuring governance is applied uniformly at scale.

Advise senior stakeholders — synthesize governance risk, compliance gaps, and architectural trade-offs into clear, well-reasoned positions for technical and executive audiences. Be the voice that keeps leadership informed and aligned on where the risks are and how they are being addressed.

Minimum Qualifications

• Deep experience in data governance, ML governance, or compliance architecture within complex, large-scale technical environments.
• Deep understanding of ML workflows, data engineering, and ML infrastructure — drive and provide feedback in design and architectural discussions.
• Proven track record of working cross-functionally with legal, privacy, compliance, and engineering teams to define and implement governance solutions.
• Demonstrated ability to refine legal and regulatory requirements into technical architecture and engineering deliverables.
• Fluent in both legal and engineering languages — able to take prescriptive or loosely defined compliance obligations and translate them into precise technical requirements without losing the spirit of the original intent.
• Experience engaging upstream with legal and policy stakeholders to shape policy guidance, ensuring that it is technically coherent.
• Exceptional ability to operate in ambiguity — connecting requirements that don't come pre-connected, and driving clarity across organizations with different vocabularies and priorities.

Preferred Qualifications

• Familiarity with AI/ML regulatory frameworks and emerging legislation (e.g., EU AI Act, GDPR, CCPA, data residency requirements).
• Experience designing or implementing data classification systems, data catalogs, or model registries in a production ML environment.
• Experience building governance frameworks for internal developer platforms or MLOps tooling.
• Comfortable presenting complex compliance and architectural topics to senior and executive engineering and legal audiences.
• Bachelor’s degree in Computer Science or similar degree. Master's degree preferred.