Engineering Manager

Summary

Imagine what you could do here. At Apple, we believe new insights have a way of becoming excellent products, services, and customer experiences very quickly. Bring passion and dedication to your job and there’s no telling what you could accomplish. The people here at Apple don’t just build products — they build the kind of wonder that’s revolutionized entire industries. It’s the diversity of those people and their ideas that inspires the innovation that runs through everything we do, from amazing technology to industry-leading environmental efforts. Join Apple, and help us leave the world better than we found it. Manufacturing Systems and Infrastructure (MSI) team is an engineering organization under the Product Operations org. MSI is responsible for the design, development, and maintenance of systems tools, services, and applications required to efficiently run manufacturing operations at scale across global factory sites. The ideal candidate is a strong engineering leader with deep expertise in data platforms, AI data engineering, and modern AI application architectures. This leader will build and scale the foundational capabilities for AI data lifecycle management—including data ingestion, curation, validation, quality, governance, metadata management, and observability—while delivering scalable AI-powered data products and platforms. Success in this role requires close partnership with AI/ML, product, and business teams to accelerate the development of high-quality data assets that fuel enterprise AI innovation.

Minimum Qualifications

Experience leading solution and data engineering teams building large-scale, production-grade systems. Strong background in data engineering, distributed systems, and cloud-based data platforms. Proven experience designing and building scalable data products, data pipelines, APIs, and platform services. Experience with AI data lifecycle management, including dataset curation, validation, quality evaluation, metadata management, lineage, and governance. Strong expertise in AI Data Platforms & Engineering, including Python, SQL, Spark, Airflow, Kafka, data pipelines, distributed systems, and modern data lake/lakehouse architectures. Experience building AI Data Curation & Quality capabilities, including dataset engineering, validation frameworks, data profiling, observability, and data quality metrics. Understanding of GenAI enablement technologies, including LLM applications, RAG architectures, embeddings, and AI agent workflows. Experience with cloud and infrastructure technologies, including AWS/GCP/Azure, Kubernetes, Docker, CI/CD, and scalable production systems. Demonstrated ability to define technical strategy, drive architecture decisions, lead complex execution, and collaborate effectively across cross-functional teams.

Preferred Qualifications

Experience building AI data platforms or infrastructure supporting LLM applications and AI products. Experience with multimodal data including text, image, video, sensor, or operational datasets. Experience building data quality frameworks, AI evaluation pipelines, or dataset management platforms. Experience translating emerging AI technologies into scalable enterprise solutions. Strong communication skills with the ability to influence technical direction across organizations.

Description

As the Engineering Manager, AI Data Platforms & Quality within the MSI organization, you will lead the team's expanded charter to build next-generation AI data products, data platforms, and data quality capabilities that enable GenAI, agentic AI, and embodied AI initiatives. You will define and drive the technical strategy for transforming enterprise, operational, and multimodal data into trusted, AI-ready assets that power intelligent applications, AI agents, analytics, and automated workflows.

Responsibilities

Lead and grow a high-performing engineering team focused on AI data platforms, data products, data quality and GenAI solutions. Define technical vision, architecture, and roadmap for scalable AI data platforms and AI-ready data capabilities. Build reusable platform capabilities for data ingestion, processing, curation, validation, data quality management, metadata, lineage, and governance. Drive development of AI-ready datasets supporting GenAI, agentic workflows, and embodied AI applications. Enable AI applications through RAG pipelines, embeddings, vector search, knowledge systems, and AI data services. Establish engineering best practices for scalability, reliability, observability, security, and operational excellence. Partner with AI/ML engineers, product teams, and business stakeholders to identify opportunities and deliver impactful AI solutions. Mentor engineers, develop technical talent, and foster a culture of innovation and engineering excellence.