Full-time
On-device ML Infrastructure Engineer (Compiler & Runtime)
Posted by Apple • Cupertino, CA, United States
About the Role
**Role Number:** 200630418-0836
**Summary**
Imagine being at the forefront of an evolution where modern AI meets the elegance of Apple silicon. The On-Device Machine Learning team transforms groundbreaking research into practical applications, enabling billions of Apple devices to run powerful AI models locally, privately, and efficiently. We stand at the unique intersection of research, software engineering, hardware engineering, and product development, making Apple the leading destination for machine learning innovation.
Our team builds the essential infrastructure that enables machine learning at scale on Apple devices. This involves onboarding powerful architectures to embedded systems, developing optimization toolkits for model compression and acceleration, building ML compilers and runtimes for efficient execution, and creating comprehensive benchmarking and debugging toolchains. This infrastructure forms the backbone of Apple’s machine learning workflows across ...
**Summary**
Imagine being at the forefront of an evolution where modern AI meets the elegance of Apple silicon. The On-Device Machine Learning team transforms groundbreaking research into practical applications, enabling billions of Apple devices to run powerful AI models locally, privately, and efficiently. We stand at the unique intersection of research, software engineering, hardware engineering, and product development, making Apple the leading destination for machine learning innovation.
Our team builds the essential infrastructure that enables machine learning at scale on Apple devices. This involves onboarding powerful architectures to embedded systems, developing optimization toolkits for model compression and acceleration, building ML compilers and runtimes for efficient execution, and creating comprehensive benchmarking and debugging toolchains. This infrastructure forms the backbone of Apple’s machine learning workflows across ...
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