Machine Learning Engineer, Platform Architecture

Apple • Cupertino, CA

3d

About The Position

The Platform Architecture group connects Apple hardware and software into a unified system. This role 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, optimized solutions for ML applications. The engineer explores ways to map ML workloads to Apple silicon and develops performance models and simulations to inform and validate architecture decisions. The role also investigates how workloads can run efficiently on Apple SoCs and communicates findings to software and algorithm teams.

Responsibilities include creating optimized implementations of ML workloads on Apple silicon, including Neural Engine, GPU, and CPU; collaborating with IP and SoC architecture teams on performance models and simulations of future hardware; conducting performance studies to inform and validate architecture decisions; and working with system teams to create high-level performance models of emerging ML techniques and analyze system architecture trade-offs.

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 ML hardware accelerators or processing units including GPU, image/video, or CPUs.

Nice To Haves

• MS or PhD in EE/CE/CS or a related field and 3+ years of relevant experience.
• Experience with ML frameworks such as PyTorch and efficient implementations of machine learning algorithms.
• Experience optimizing and deploying ML models and/or runtime frameworks in production inference/training environments.
• Experience designing experiments to evaluate ML 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.
• Explore ways to map machine learning workloads to Apple silicon and communicate findings to software and algorithm teams.

What This Job Offers

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