Apple Data Platform serves as a foundational building block for Apple’s Services, Software, and AI/ML ecosystem, providing advanced data governance, compliance, and scalable data management. It offers a comprehensive suite of capabilities, including batch and real-time data processing, embeddings management, feature stores, lakehouse architecture, data virtualization, and inference as a service. These tools empower analytics and AI workflows across Apple’s ecosystem, driving seamless integration and innovation across products and services. Leveraging open-source technologies such as Ray, Spark, Flink, and Iceberg across multi-cloud and on-premises environments, the Apple Data Platform powers data-driven intelligence that continuously enhances the customer experience. We are seeking a Senior Engineering Leader with a passion for delivering scalable, secure, and user-friendly infrastructure for all of Data and AI platforms.

DESCRIPTION

As a Senior Engineering Manager, you will lead teams responsible for building and managing platforms that provide compute abstraction, advanced scheduling, cost efficiency, observability, and capacity management for all Data and AI workloads at Apple. RESPONSIBILITIES: - Drive the overall strategy and execution of the ADP Compute Platform. - Collaborate with key stakeholders and partners to deliver integrations, compliance, cost efficiency, and an optimal experience. - Partner with internal customers and product teams to drive the platform roadmap and accelerate adoption of the platform. - Lead and mentor a team of engineers, providing guidance, support, and professional development opportunities. - Cultivate a culture of collaboration, innovation, and continuous learning within the team. - Identify opportunities and drive a strategic approach to contributing to open source.

MINIMUM QUALIFICATIONS

• 12+ years of software development experience in Data and AI platforms and infrastructure.
• 8+ years of engineering people management experience-leading, mentoring, and growing teams.
• 10+ years of experience building and operationalizing data platform technologies on AWS, Azure, GCP, and on-premises infrastructures.
• Strong leadership in cross-functional collaboration, driving alignment across engineering, product, and business teams to accelerate platform adoption.
• Expertise in distributed systems and cloud-native infrastructure, including Kubernetes, serverless computing, and container orchestration.
• Experience designing and optimizing large-scale compute platforms with advanced scheduling, resource management, and cost efficiency considerations.
• BS, MS, or Ph.D. in Computer Science or a related field of study.

PREFERRED QUALIFICATIONS

• Expertise in AI/ML workload orchestration and scheduling, including optimizing resource allocation for large-scale training, inference, and data processing pipelines.
• Experience building and managing AI infrastructure platforms, integrating frameworks like TensorFlow, PyTorch, Ray, and Spark with distributed compute environments.
• Deep understanding of AI cost efficiency and performance optimization, leveraging techniques such as model parallelism, heterogeneous compute scheduling (GPUs, TPUs), and auto-scaling for AI workloads.

Please refer the Job description for details