Sr Machine Learning Manager, System

San Francisco Bay Area, California, United States Machine Learning and AI

The Apple Intelligence Platform team builds the foundational on-device software infrastructure that powers Apple Intelligence. We develop the APIs, platforms, and systems that enable breakthrough features like Writing Tools, Siri, Visual Intelligence, and Image Playground. Our work spans the full software stack — from low‑level inference engines and runtime optimization to high‑level platform APIs like the Foundation Models API. Our mission is to build production‑ready software platforms that deliver magical experiences to millions of users.

We work closely with research teams to understand new ML techniques, then focus on the engineering challenges of building robust, scalable systems to ship them. Whether it’s designing APIs for agentic workflows, optimizing inference pipelines, implementing efficient caching strategies for attention mechanisms, or creating infrastructure for search and retrieval, we’re focused on the platform engineering and software architectures that make Apple Intelligence possible.

We value strong software engineering fundamentals combined with deep understanding of ML systems and techniques. Our team members know how to build production platforms that are reliable, performant, and maintainable at scale, while also understanding the ML primitives — transformers, attention, KV caches, kernels — that power these systems. We’re looking for a leader who can drive platform development, build strong engineering teams, and deliver the software infrastructure that powers Apple Intelligence features used by millions every day.

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
• 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…