On-device ML Infrastructure Engineer (ML Performance Insights)

Job Summary

Summary Posted: Aug 21, 2025 Weekly Hours: 40 Role Number: 20... The On-Device Machine Learning team at Apple is responsible for enabling the Research to Production lifecycle of innovative machine learning models that power magical user experiences on Apples hardware and software platforms. Apple is the best place to do on-device machine learning, and this team sits at the heart of that subject area, collaborating 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 implement 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 Apples critical machine learning workflows across Camera, Siri, Health, Vision, etc., and as such is an integral part of Apple Intelligence.Our group is seeking an ML Infrastructure Engineer, with a focus on ML Performance Insights. The role entails scaling and extending a significant on-device ML benchmarking service used across Apple.

Description

Description This role provides a great opportunity to help scale and extend an on-device ML benchmarking service that is used across Apple, in support of a range of devices from small wearables up to the largest Apple Silicon Macs.The role contributes to building the first end-to-end developer experience for ML development that, by taking advantage of Apples vertical integration, allows developers to iterate on model authoring, optimization, transformation, execution, debugging, profiling and analysis. The role further offers a learning platform to dig into the latest research about on-device machine learning, an exciting ML frontier! Possible example areas include model visualization, efficient inference algorithms, model compression, on-device fine-tuning, federated learning and/or ML compilers/run-time.

EEO Notice

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