On-Device ML Infrastructure Engineer, ML User Experience, APIs & Integration, 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 a top destination for machine learning are building the first end-to-end developer experience for ML development that by taking advantage of Apples vertical integration allows developers to iterate on model authoring optimization transformation execution debugging profiling and team builds the essential infrastructure that enables machine learning at scale on Apple devices. This involves onboarding modern 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 supplying 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 platforms.n

Our group is seeking an ML Infrastructure Engineer with a focus on ML user experience APIs and integration. The role is responsible for developing new ML model conversion and authoring APIs that serve as the main entry point into Apples ML infrastructure. nnAs an engineer in this role you will be primarily focused on developing and using APIs that enable ML engineers to efficiently author and convert ML models to run effectively on Apple platforms. You will integrate Apples ML tools/APIs into internal and external model repositories to evaluate and demonstrate how models can be efficiently ingested and implemented within Apples ML stack. You will ideate design and stress test a variety of optimizations required to support these models ranging from source-level optimizations (e.g. in the PyTorch program) to custom transformations within Apples model a power user of Apples ML infrastructure you will also help create the latest and most capable models with strong driven performance across hardware targetsshowcasing the practical power of Apples authoring and runtime APIs. This role offers the opportunity to shape how ML developers experience Apples end-to-end inference stack from model creation to role requires a confirmed understanding of ML modeling (architectures training vs. inference trade-offs etc.) ML deployment optimizations (e.g. quantization) and strong experience designing Python APIs.n

Develop APIs in Apples ML stack for ML engineers to efficiently import and implement their Apples ML tools into internal and external model repositories to demonstrate and stress-test model ingestion with peak efficiency and optimizations across the pipeline including source-level transformations and custom operations to improve inference the latest ML models with peak performance and use these examples to highlight and validate the authoring and runtime capabilities of Apples inference stack.n

Bachelors in Computer Sciences Engineering or related subject proficient in Python programming familiarity with C is in at least one ML authoring framework such as PyTorch MLX and understanding of ML fundamentals including common architectures such as -on experience with ML inference optimizations such as quantization pruning KV caching communication skills including ability to connect with multi-functional audiences.n

Experience with C Swift and/or GPU programming with QAT and other compression and quantization techniques employing PyTorch designing Python APIs and deploying production-grade Python with MLIR/LLVM or similar compiler with Hugging Face or other model repositories.n