Sr Machine Learning Manager, Intelligent System Experience

As a Senior Machine Learning Manager on the Apple Intelligence Platform team, you will lead a team of engineers building critical platform infrastructure and APIs that power features used by millions of Apple customers daily. You'll be responsible for the technical execution and delivery of software systems that span the platform stack — from runtime engines and inference pipelines to developer-facing APIs and service integrations. This role requires someone who deeply understands both ML fundamentals and platform engineering, able to make informed architectural decisions about how to build software systems that efficiently support modern ML techniques. You will work cross-functionally with researchers, product teams, and platform engineers to build the production software systems needed to ship new capabilities. Success in this role means delivering robust, well-architected platforms that leverage your understanding of ML to enable world-class Apple Intelligence experiences. Lead and grow a team of platform engineers building ML infrastructure, APIs, and services for Apple Intelligence Build production software platforms to support new ML techniques, translating research innovations into shippable, scalable systems Ensure platforms meet Apple's standards for performance, reliability, scalability, privacy, and user experience Make large-scale architectural decisions Guide development of APIs and frameworks for agentic workflows and complex multi-step ML systems Establish engineering excellence through software architecture standards, code quality practices, testing, and operational rigor Mentor engineers on platform engineering, ML systems implementation, API design, and production software best practices Partner with product and engineering leaders to align platform capabilities with feature requirements and business goals 8+ years of experience in ML platform engineering, ML infrastructure, or related fields, with 3+ years in technical leadership or management roles Deep understanding of ML fundamentals including neural network architectures, transformers, attention mechanisms, and inference optimization Strong software engineering fundamentals with expertise in systems design, API architecture, and distributed systems Proven experience building and shipping production ML APIs, platforms, or infrastructure at scale Strong knowledge of ML software stacks and modeling primitives: KV caching, kernel methods, attention architectures, and efficient inference techniques Hands-on experience with ML frameworks (PyTorch, TensorFlow, JAX), serving systems, and production ML deployment Understanding of modern agentic workflows, multi-step reasoning systems, and API design for complex ML applications Track record of leading engineering teams and delivering complex ML platform projects from conception to production Strong architectural skills with experience making technical decisions for large-scale, high-performance ML systems Excellent communication and collaboration skills with ability to work across research, product, and engineering teams BS, MS, or PhD in Computer Science, Machine Learning, or related field (or equivalent industry experience)

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