On-device ML Infrastructure Engineer (Fine-tuning and Customization)
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 the development of Apple's Create ML family of tools. The role entails designing, implementing and maintaining workflows for fine-tuning ML models and Apple Intelligence with data.
Description
We are building an 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. This role focuses on the Create ML framework for machine learning model and Apple Intelligence customization. We're looking for a highly motivated software engineer that is creative, talented, and passionate about providing high quality developer tools, workflows and APIs in the fast paced and dynamic space of ML.
Key responsibilities:
Designing and implementing workflows for fine-tuning and adapting ML models
Architecting and maintaining the internals of the Create ML framework
Working with internal and external developers to enable their uses cases powered by on-device ML
Triaging and addressing framework performance issues and functionality gaps
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 the development of Apple's Create ML family of tools. The role entails designing, implementing and maintaining workflows for fine-tuning ML models and Apple Intelligence with data.
Description
We are building an 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. This role focuses on the Create ML framework for machine learning model and Apple Intelligence customization. We're looking for a highly motivated software engineer that is creative, talented, and passionate about providing high quality developer tools, workflows and APIs in the fast paced and dynamic space of ML.
Key responsibilities:
Designing and implementing workflows for fine-tuning and adapting ML models
Architecting and maintaining the internals of the Create ML framework
Working with internal and external developers to enable their uses cases powered by on-device ML
Triaging and addressing framework performance issues and functionality gaps