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 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 Apples machine learning workflows across Camera Siri Health Vision and other core experiences contributing to the overall Apple Intelligence 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 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.n

Were building an end-to-end developer experience for machine learning development that leverages Apples 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. Were 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.

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 with teams across the company to support advanced use cases for Siri Apple Intelligence Camera and changes in our authoring and MLIR-based compiler to expose mechanisms for state-of-the-art model executionnImplement mechanisms to support efficient orchestration of models across Apple Silicon hardware.

3-5 years working on tooling built in Python 3 and C/SwiftnFamiliarity with common ML model architectures execution schemes and with PyTorch or related training frameworks.

Experience working on or adjacent to MLIR-based with deploying applications or tooling on Apple with programming paradigms for the GPU CPU and Neural with writing kernels for ML model execution.