On-device ML Infrastructure Engineer (ML User Experience APIs u0026 Integration)

Apple is the best place to do on-device machine learning, and this team sits at the heart of that discipline, interfacing with research, SW engineering, HW engineering, and products. This amazing team is responsible for enabling the Research to Production lifecycle of cutting edge ML models that power magical user experiences on Apple's hardware and software platforms. We build critical infrastructure that begins with onboarding the latest machine learning architectures to embedded devices, optimization toolkits to better suit target devices, machine learning compilers and runtimes that execute these models as efficiently. Our team also benchmark, analyze and debugging toolchains needed to improve on new model iterations. This infrastructure underpins most of Apple's critical machine learning workflows across Camera, Siri, Health, Vision, etc. Our team plays an integral part of Apple Intelligence. Our group is looking for an ML Infrastructure Engineer, with a focus on ML user experience APIs and Integration. Our team is responsible for developing new ML model conversion u0026 authoring APIs that will be a part of CoreML tools (CoreML's authoring/conversion toolkit). This role takes ownership for integrating the APIs into internal and external systems (e.g., HuggingFace.)As an engineer in this role, you will focused on developing, using APIs in Core ML tools that enable ML engineers to efficiently author/convert ML models to CoreML. You will ideate, design, and stress test the gamut of optimizations required to ingest these models, ranging from source level optimizations (e.g., in the PyTorch program), to custom optimizations after converting to CoreML's model representation. We are building the first end-to-end developer experience for ML development that, by taking advantage of Apple's vertical integration, allows developers to iterate on model authoring, optimization, transformation, execution, debugging, profiling and analysis. The coremltools authoring and conversion APIs are the entrypoint to the rest of the infrastructure stack. We are looking for someone who is highly self motivated and passionate about ML modeling (architectures, training vs inference trade-offs, etc.), ML deployment optimizations (e.g., quantization). If you have a proven track record of developing and working with the internals of an ML python library, writing high quality code and shipping software, we strongly encourage you to apply.Experience with any on-device ML stack, such as TFLite, ONNX, etc.

Experience with designing Python APIs and production deployment of python packages is a strong plus.

Experience with HuggingFace or any other model repository

Experience with MLIR/LLVM or any compiler toolchains

Good communication skills, including ability to communicate with cross-functional audiences.Array