About The Position

Apple’s Platform Architecture group connects hardware and software into a unified system. The group 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 solutions for machine learning applications. 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 identify how workloads can run efficiently on Apple SoCs, and communicate findings to software and algorithm teams.

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 GPU, image/video, CPUs, or similar.

Nice To Haves

  • MS or PhD in EE/CE/CS or a related field and 10+ years of relevant experience.
  • Experience with machine learning frameworks (e.g. 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.
  • 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.
  • 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.
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