On-Device ML Compiler Engineer, Model Compilation, Graphics, Games and Machine Learning

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 the leading destination for machine learning team builds the essential infrastructure that enables machine learning at scale on Apple devices. This involves onboarding innovative 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 a great 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 compilation. The role entails working closely with model authoring runtime and performance teams to ensure that models can bring to bear the full capabilities of the hardware.n

Were building an end-to-end developer experience for machine learning development that employs 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. nnWere seeking a highly motivated software engineer who is creative versatile and passionate about machine learning common compiler optimizations and system software engineering in the fast-paced and dynamic field of machine learning. We have an MLIR-based compiler stack and use it to target the neural engine GPU and CPU in order to harness the full capabilities of the system for ML workflows and

Inspire changes in our MLIR-based compiler in order to target improved runtime performance by demonstrating the capabilities of the upstream changes in MLIR to better support new features and workflows in the hardware that lead to more optimal execution performance across all types of devices and device core pieces of the compiler stack enabling heterogeneous compute across Apple devices. We target execution of ML models across the Apple ecosystem from resource-constrained devices like Apple Watch to the high-end Macs with Ultra closely with hardware software and performance teams across the company to accelerate and optimize execution by taking advantage of the latest features in the hardware OS and drivers.

3-5 years working on MLIR-based with common ML model architectures execution schemes and with CnnFamiliarity with PyTorch or related training frameworksn

Familiarity with with programming paradigms for the GPU CPU and Neural with writing kernels for ML model execution.