On-device ML Infrastructure Engineer (ML Compiler Frontend)
Apple
Software Engineering, Other Engineering, Data Science
Cupertino, CA, USA
Posted on May 30, 2025
The On-Device Machine Learning team at Apple is responsible for enabling the Research to Production lifecycle of cutting edge machine learning models that power magical user experiences on Apple’s hardware and software platforms. 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. The team builds critical infrastructure that begins with onboarding the latest machine learning architectures to embedded devices, optimization toolkits to optimize these models to better suit the target devices, machine learning compilers and runtimes to execute these models as efficiently as possible, and the benchmarking, analysis and debugging toolchain needed to improve on new model iterations. This infrastructure underpins most of Apple’s critical machine learning workflows across Camera, Siri, Health, Vision, etc., and as such is an integral part of Apple Intelligence. Our group is looking for an ML Infrastructure Engineer, with a focus on ML model semantics and frontend stages of ML compilation. The role is responsible for working with ML research and Applied research engineers to onboard the newest ML architectures to CoreML’s ML model representation, including evolving the representation to support the latest and greatest features in the authored ML program (e.g., PyTorch), and develop the frontend stages of CoreML’s model compilation pipelines.