On-device ML Infrastructure Engineer (ML User Experience APIs)
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 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 user experience APIs. The role is responsible for developing new ML model conversion & authoring APIs that will be a part of coremltools (CoreML's authoring/conversion toolkit). The role also requires interacting with ML authoring frontend (like PyTorch) to capture graphs from the frontend, and efficiently import these graphs into CoreML stack. The conversion and authoring APIs also include feedback APIs, whereby ML engineers can evaluate/visualize the converted models to evaluate functionality/performance.
Description
As an engineer in this role, you will be primarily focused on developing APIs in coremltools to enable ML engineers to efficiently author/convert ML models to CoreML, including any feedback APIs to help them evaluate the CoreML programs. The conversion APIs also include optimizations to enable peak performance on Apple devices, such as quantization, compression, distillation, etc. The role requires a good understanding of ML modeling (architectures, training vs inference trade-offs, etc.), and a good understanding of designing Python APIs and packages.
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. The core ml tools authoring and conversion APIs are the entry-point to the rest of the infrastructure stack.
Key responsibilities:
Develop APIs in coremltools for ML engineers to efficiently convert models from ML frontends (such as PyTorch, JAX) into CoreML's model representation.
Develop APIs in coremltools for ML engineers to author/tailor programs to achieve peak performance on Apple devices (e.g., quantization, distillation, custom operations, etc.)
Develop APIs, and tools for ML engineers to evaluate and converted/authored programs.
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 user experience APIs. The role is responsible for developing new ML model conversion & authoring APIs that will be a part of coremltools (CoreML's authoring/conversion toolkit). The role also requires interacting with ML authoring frontend (like PyTorch) to capture graphs from the frontend, and efficiently import these graphs into CoreML stack. The conversion and authoring APIs also include feedback APIs, whereby ML engineers can evaluate/visualize the converted models to evaluate functionality/performance.
Description
As an engineer in this role, you will be primarily focused on developing APIs in coremltools to enable ML engineers to efficiently author/convert ML models to CoreML, including any feedback APIs to help them evaluate the CoreML programs. The conversion APIs also include optimizations to enable peak performance on Apple devices, such as quantization, compression, distillation, etc. The role requires a good understanding of ML modeling (architectures, training vs inference trade-offs, etc.), and a good understanding of designing Python APIs and packages.
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. The core ml tools authoring and conversion APIs are the entry-point to the rest of the infrastructure stack.
Key responsibilities:
Develop APIs in coremltools for ML engineers to efficiently convert models from ML frontends (such as PyTorch, JAX) into CoreML's model representation.
Develop APIs in coremltools for ML engineers to author/tailor programs to achieve peak performance on Apple devices (e.g., quantization, distillation, custom operations, etc.)
Develop APIs, and tools for ML engineers to evaluate and converted/authored programs.