On-Device ML Infrastructure Engineer (ML Performance Insights)

Apple • Seattle, WA

74d

About The Position

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 Performance Insights. The role entails scaling and extending a significant on-device ML benchmarking service used across Apple. 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. In this role, you will be an integral member of a talented team that is 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 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.

Requirements

• Masters or PhDs in Computer Science or relevant disciplines.
• Experience with on-device ML frameworks such as CoreML, TFLite or ExecuTorch.
• Experience with any ML authoring framework (PyTorch, TensorFlow, JAX, etc.) is a strong plus.
• Experience in software architecture, APIs, high performance extensible software and scalable software systems.
• Understanding of how to optimize code run time and throughput for a given platform.

Nice To Haves

• Interest and experience in power and/or hardware accelerators is a plus.
• Back-end system skills including containers (docker), cloud orchestration (Kubernetes), database (SQL, Postgres).

Responsibilities

• Scale and extend an on-device ML benchmarking service used across Apple.
• Build critical infrastructure for onboarding machine learning architectures to embedded devices.
• Develop optimization toolkits for model optimization on target devices.
• Create machine learning compilers and runtimes for efficient model execution.
• Develop benchmarking, analysis, and debugging toolchains for model iterations.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Industry: Computer and Electronic Product Manufacturing
• Education Level: Master's degree
• Number of Employees: 5,001-10,000 employees