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# On-Device ML Infrastructure Engineer (ML User Experience APIs), Graphics, Games and Machine Learning

- **Company:** Apple Inc.
- **Location:** Cupertino, CA, United States
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), C++ (Programming Language), Computer Engineering, Python (Programming Language), Machine Learning, Pytorch, Information Technology, HuggingFace, Machine Learning Operations, Api Design
- **Published:** August 9, 2026
- **Apply:** https://us.experteer.com/career/view-jobs/on-device-ml-infrastructure-engineer-ml-user-experience-apis-graphics-games-and-machine-learning-cupertino-ca-usa-58867256

## About the Role

Experteer Overview In this role you will help shape Apple's ML infrastructure by building model conversion and authoring APIs for end-to-end model deployment on Apple platforms. You will demonstrate and optimize cross-ecosystem workflows, integrating models from external repositories like Hugging Face with Apple's stack. You'll design and stress-test optimizations across source-level code and Apple representations to achieve strong performance on diverse hardware. This is a hands-on, impact-driven position at the intersection of research, software, and hardware engineering, focused on delivering a premier on-device ML experience. Compensation / Benefits * Develop and expose ML model conversion and authoring APIs as the main entry point into Apple's ML infrastructure * Onboard popular ML models via end-to-end workflows highlighting authoring and runtime capabilities * Integrate Apple ML tools into internal and external model repositories (e.g., Hugging Face) * Ideate, design and stress
test optimizations from PyTorch programs to custom transformations in Apple's model representation * Support engineering of end-to-end inference stack from model creation to deployment Tasks * Confirmed understanding of ML modeling (architectures, training vs. inference trade-offs) * Experience in ML deployment optimizations (quantization) * Strong Python API design experience * Proficiency in Python and familiarity with C++ * Experience with ML authoring frameworks (e.g., PyTorch, MLX, JAX) * Experience with MLIR/LLVM or similar compiler toolchains * Familiarity with Hugging Face or other model repositories * Bachelor in Computer Science, Engineering, or related subject area * Hands-on experience with ML inference optimizations (quantization, pruning, KV caching) * Strong communication skills and ability to engage multi-functional audiences Key requirements *

## Description

Experteer Overview In this role you will help shape Apple's ML infrastructure by building model conversion and authoring APIs for end-to-end model deployment on Apple platforms. You will demonstrate and optimize cross-ecosystem workflows, integrating models from external repositories like Hugging Face with Apple's stack. You'll design and stress-test optimizations across source-level code and Apple representations to achieve strong performance on diverse hardware. This is a hands-on, impact-driven position at the intersection of research, software, and hardware engineering, focused on delivering a premier on-device ML experience. Compensation / Benefits * Develop and expose ML model conversion and authoring APIs as the main entry point into Apple's ML infrastructure * Onboard popular ML models via end-to-end workflows highlighting authoring and runtime capabilities * Integrate Apple ML tools into internal and external model repositories (e.g., Hugging Face) * Ideate, design and stress test optimizations from PyTorch programs to custom transformations in Apple's model representation * Support engineering of end-to-end inference stack from model creation to deployment Tasks * Confirmed understanding of ML modeling (architectures, training vs. inference trade-offs) * Experience in ML deployment optimizations (quantization) * Strong Python API design experience * Proficiency in Python and familiarity with C++ * Experience with ML authoring frameworks (e.g., PyTorch, MLX, JAX) * Experience with MLIR/LLVM or similar compiler toolchains * Familiarity with Hugging Face or other model repositories * Bachelor in Computer Science, Engineering, or related subject area * Hands-on experience with ML inference optimizations (quantization, pruning, KV caching) * Strong communication skills and ability to engage multi-functional audiences Key requirements *


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