On-Device ML Infrastructure Engineer (CoreML Runtime), Graphics, Games and Machine Learning

Imagine being at the forefront of an evolution where powerful 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. nnWe stand at the unique intersection of research software engineering hardware engineering and product development making Apple a top destination for on-device 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 Apples machine learning workflows across Camera Siri Health Vision and other core experiences contributing to the overall Apple Intelligence ecosystem. nnIf you are passionate about the technical challenges of running sophisticated ML models on resource-constrained devices and eager to directly impact how machine learning operates across the Apple ecosystem this role presents an incredible opportunity to work on the next generation of intelligent experiences on Apple platforms. nnWe are seeking an ML Infrastructure Engineer with a specific focus on graph compilers and runtimes. If you are a highly motivated software engineer who is creative versatile and passionate about machine learning operator primitives common compiler optimizations runtimes and system software engineering in the fast-paced and dynamic field of machine learning this could be a fantastic role for you.

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. nnThis role focuses on the Core ML Runtime for execution this role you will build the worlds most advanced ML graph compilation and runtime system capable of optimizing and delivering ML models efficiently on Apple products and services.

Architect and maintain the on-device graph compiler runtime and kernels for delivering ML production-critical system software for implementing ML models on Apple SiliconnProactively identify and resolve functionality model execution for various system objectives like performance energy efficiency and thermal management.

Masters or equivalent experience in Computer Sciences Engineering or related subject proficient in C or Swift. Familiarity with with any compiler stack (MLIR/LLVM/TVM/...).nFamiliarity with Operating Systems embedding programming parallel understanding of ML fundamentals including common architectures such as communication skills including ability to communicate with multi-functional audiences.

Experience with any on-device ML stack such as TFLite ONNX ExecuTorch with any ML authoring framework (PyTorch TensorFlow JAX etc.) is a strong with accelerators GPU programming is a strong plus.