On-Device ML Infrastructure Engineer, ML User Experience, APIs & Integration, Graphics, Games & ML

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 Apple’s ML infrastructure. As 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 Apple’s ML tools/APIs into internal and external model repositories to evaluate and demonstrate how models can be efficiently ingested and implemented within Apple’s 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 Apple’s model representation. As a power user of Apple’s ML infrastructure, you will also help create the latest and most capable models with strong, driven performance across hardware targets—showcasing the practical power of Apple’s authoring and runtime APIs. This role offers the opportunity to shape how ML developers experience Apple’s end-to-end inference stack, from model creation to deployment. The 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. Develop APIs in Apple’s ML stack for ML engineers to efficiently import and implement their models. Integrate Apple’s ML tools into internal and external model repositories to demonstrate and stress-test model ingestion with peak efficiency and performance. Develop optimizations across the pipeline, including source-level transformations, and custom operations to improve inference efficiency. Onboard the latest ML models with peak performance, and use these examples to highlight and validate the authoring and runtime capabilities of Apple’s inference stack. Bachelors in Computer Sciences, Engineering, or related subject area. Highly proficient in Python programming, familiarity with C++ is required. Proficiency in at least one ML authoring framework, such as PyTorch, MLX, and JAX. Strong understanding of ML fundamentals, including common architectures such as Transformers. Hands-on experience with ML inference optimizations, such as quantization, pruning, KV caching, etc. Strong communication skills, including ability to connect with multi-functional audiences. Experience with C++, Swift, and/or GPU programming paradigms. Familiarity with QAT and other compression and quantization techniques employing PyTorch workflows. Experience designing Python APIs and deploying production-grade Python packages. Experience with MLIR/LLVM or similar compiler toolchains. Familiarity with Hugging Face or other model repositories.

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