On-device ML Infrastructure Engineer (ML User Experience APIs & Integration)

Imagine being at the forefront of an evolution where innovative AI meets the elegance of Apple silicon. The On-Device Machine Learning team transforms groundbreaking research into practical applications, enabling billions of Apple devices to run powerful AI models locally, privately, and efficiently. We stand at the unique intersection of research, software engineering, hardware engineering, and product development, making Apple a top destination for machine learning innovation. This team builds the essential infrastructure that enables machine learning at scale on Apple devices. This involves onboarding modern architectures to embedded systems, developing optimization toolkits for model compression and acceleration, building ML compilers and runtimes for efficient execution, and creating comprehensive benchmarking and debugging toolchains. This infrastructure forms the backbone of Apple’s machine learning workflows across Camera, Siri, Health, Vision, and other core experiences, supplying to the overall Apple Intelligence ecosystem. If you are passionate about the technical challenges of running sophisticated ML models across all devices, from resource-constrained devices to powerful clusters, and eager to directly impact how machine learning operates across the Apple ecosystem, this role presents a great opportunity to work on the next generation of intelligent experiences on Apple platforms. Our group is seeking an ML Infrastructure Engineer, with a focus on ML user experience APIs and integration. The role is responsible for developing new ML model conversion and authoring APIs that serve as the main entry point into Apple’s ML infrastructure. An engineer in this role will also drive the onboarding of popular and latest ML models—demonstrating end-to-end workflows that highlight both the authoring and runtime capabilities of Apple’s ML ecosystem with strong, competitive performance on Apple platforms. The role also involves integrating these APIs into internal and external systems (e.g., Hugging Face) to showcase the most efficient path for bringing models into Apple’s ML stack. This integration could involve a gamut of optimizations ranging from authored program optimizations (e.g., in PyTorch) to custom transformations within Apple’s model representation.