Machine Learning Engineer, Platform Architecture

Apple • Cupertino, CA

3d

About The Position

Apple’s Platform Architecture group connects hardware and software into a unified system. The team works at the intersection of machine learning applications and Apple silicon architecture, collaborating with SoC/IP architecture, system, software, and algorithm teams to develop integrated, highly optimized machine learning solutions.

In this role, you will explore ways to map machine learning workloads to Apple silicon and develop performance models and simulations. Your work will inform and validate architecture decisions, help determine how workloads can run efficiently on Apple SoCs, and communicate findings to software and algorithm teams. You will also evaluate emerging model optimization techniques and propose ideas that inform hardware and algorithm direction.

Requirements

• Bachelor’s degree.
• Programming experience in C/C++ and/or Python.
• Experience in computer architecture.
• Experience with at least one hardware IP, such as machine learning hardware accelerators or processing units such as GPUs, image/video processors, or CPUs.
• MS or PhD in EE/CE/CS or a related field and 10+ years of relevant experience.
• Verbal and written communication skills for collaborating with partner teams.
• Ability to prototype algorithms on CPU/GPU/Neural Engine, analyze performance metrics, and create high-level complexity models.

Nice To Haves

• Experience with machine learning frameworks such as PyTorch and efficient implementations of machine learning algorithms.
• Experience optimizing and deploying machine learning models and/or runtime frameworks in production inference/training environments.
• Experience designing experiments to evaluate machine learning model optimization techniques.
• Experience creating SoC or IP performance models/simulations.
• Understanding of compiler frameworks/technologies.

Responsibilities

• Create optimized implementations of machine learning workloads on Apple silicon, including Neural Engine, GPU, and CPU.
• Collaborate with IP and SoC architecture teams to develop performance models and simulations of future hardware.
• Conduct performance studies to inform and validate architecture decisions.
• Collaborate with system teams to create high-level performance models of emerging machine learning techniques and analyze system architecture trade-offs.
• Evaluate emerging machine learning model optimization techniques through experimentation and analysis, and propose ideas to inform hardware and algorithm direction.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior