On-device ML Infrastructure Engineer, Compiler & Runtime, Graphics, Games & ML

We are seeking an experienced ML Infrastructure Engineer with a specific focus on building the best execution engine and compilation toolchain that employs our compilers infrastructure and the world’s most efficient, portable, and extensible runtime, and which is capable of optimizing and driving ML models efficiently on Apple products and services, current and future. This is a senior role and functions as the glue between our compiler technology, the runtime components, the kernel libraries, and the low-level hardware compilers to enable the execution of ML across a wide variety of devices and use cases. The successful candidate will make critical decisions affecting project direction and outcome. We’re seeking a highly motivated software engineer who is creative, skilled, and passionate about machine learning, common compiler optimizations, and system software engineering in the fast-paced and dynamic field of machine learning Help lead and deliver on critical initiatives for on device machine learning infrastructure. Design, build, and maintain critical machine learning infrastructure that powers Apple’s machine learning features. Collaborate with downstream hardware compilers to best leverage Apple’s machine learning hardware. Collaborate with first and third party users to adopt our infrastructure and apply protocols when they implement machine learning on Apple devices. Ensure our infrastructure can run optimally for a wide range of first and third party machine learning models. Bachelors in Computer Science, Engineering, or related subject area and 5+ years of hands on experience. Highly proficient in C++. Familiarity with Python and Swift. Familiarity with Operating Systems and Embedded Programming. Sound understanding of ML fundamentals, including common architectures such as Transformers. Good communication skills, including ability to communicate with multi-functional audiences. Experience with any on-device ML stack, such as TFLite, ONNX, ExecuTorch, etc. Experience with open source machine learning models (Mistral, Phi, Gemma, Huggingface, etc) Experience with any compiler stack (MLIR/LLVM/TVM/...). Experience with any ML authoring framework (PyTorch, TensorFlow, JAX, etc.). Experience with machine learning accelerators and GPU programming.

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