On-device ML Infrastructure Engineer (Compiler & Runtime)
Apply NowCompany: Apple
Location: Cupertino, CA 95014
Description:
Summary
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 Apple 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 graph compiler and runtime. The role entails building optimized compiler and runtime infrastructure that ensures first and third party machine learning models can run optimally across the range of Apple products and leverage Apple's unique machine learning hardware.
Description
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.
As an engineer in this role, you will be tasked with building critical compiler and runtime infrastructure that powers almost all on device machine learning features across Apple devices. You will have the opportunity to leverage Apple's unique vertical machine learning stack that goes all the way down to custom silicon and drive impact on a wide range of Apple features and products.
Key responsibilities:
Design, build, and maintain critical machine learning infrastructure that powers Apple's on device machine learning features.
Collaborate with downstream hardware compilers to best leverage Apple's on device machine learning hardware.
Collaborate with first and third party users to adopt our infrastructure and apply best practices when they implement machine learning on Apple devices.
Ensure our infrastructure can run optimally for a wide range of first and third party machine learning models.
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 Apple 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 graph compiler and runtime. The role entails building optimized compiler and runtime infrastructure that ensures first and third party machine learning models can run optimally across the range of Apple products and leverage Apple's unique machine learning hardware.
Description
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.
As an engineer in this role, you will be tasked with building critical compiler and runtime infrastructure that powers almost all on device machine learning features across Apple devices. You will have the opportunity to leverage Apple's unique vertical machine learning stack that goes all the way down to custom silicon and drive impact on a wide range of Apple features and products.
Key responsibilities:
Design, build, and maintain critical machine learning infrastructure that powers Apple's on device machine learning features.
Collaborate with downstream hardware compilers to best leverage Apple's on device machine learning hardware.
Collaborate with first and third party users to adopt our infrastructure and apply best practices when they implement machine learning on Apple devices.
Ensure our infrastructure can run optimally for a wide range of first and third party machine learning models.