About the job
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 innovation.
Responsibilities
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.
Qualifications
Minimum
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.
Preferred
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.