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 innovation. Our 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 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 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 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.

DESCRIPTION

We’re building an end-to-end developer experience for machine learning

development that employs 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, 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 execution.

MINIMUM QUALIFICATIONS

3-5 years working on MLIR-based compilers. Familiarity with common ML model

architectures, execution schemes, and operations. Familiarity with C++

Familiarity with PyTorch or related training frameworks

PREFERRED QUALIFICATIONS

Familiarity with Swift. Familiarity with programming paradigms for the GPU, CPU,

and Neural Engine. Familiarity with writing kernels for ML model execution.