On-device ML Infrastructure Engineer (Orchestration & Performance)

Imagine being at the forefront of an evolution where cutting-edge 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 the leading destination for machine learning innovation.

Our team builds the essential infrastructure that enables machine learning at scale on Apple devices. This involves onboarding cutting-edge 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, contributing 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 an exciting opportunity to work on the next generation of intelligent experiences on Apple platforms.

Our group is looking for an ML Infrastructure Engineer, with a focus on model orchestration and performance. The role entails working closely with model authoring, compiler, and runtime teams to ensure that our framework allows executing models with the best stability and performance.

Description

We're building an end-to-end developer experience for machine learning development that leverages Apple's vertical integration. This allows developers to iterate on model authoring, optimization, transformation, execution, debugging, profiling, and analysis. This role focuses on the core runtime for execution across a wide variety of devices and use cases. We're seeking a highly motivated software engineer who is creative, talented, and passionate about machine learning, common compiler optimizations, and system software engineering in the fast-paced and dynamic field of machine learning.

Responsibilities:

Drive full-stack changes through the OS and tooling to enable deploying large SOTA models across the Apple Silicon ecosystem, from Apple Watch to Mac Studios.

Partner with teams across the company to support advanced use cases for Siri, Apple Intelligence, Camera, and more.

Make changes in our authoring and MLIR-based compiler to expose mechanisms for state-of-the-art model execution

Implement mechanisms to support efficient orchestration of models across Apple Silicon hardware.

Preferred Qualifications

Experience working on or adjacent to MLIR-based compilers.

Familiarity with deploying applications or tooling on Apple platforms.

Familiarity with programming paradigms for the GPU, CPU, and Neural Engine.

Familiarity with writing kernels for ML model execution.

Minimum Qualifications

3-5 years working on tooling built in Python 3 and C++/Swift

Familiarity with common ML model architectures, execution schemes, and operations.

Familiarity with PyTorch or related training frameworks.