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 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. You will create optimized implementations of ML workloads on the Neural Engine, GPU, and CPU; evaluate emerging model optimization techniques; and analyze hardware, system, and algorithm 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 GPUs, image/video processors, or CPUs.
• Verbal and written communication skills for collaborating with partner teams.
• Ability to prototype algorithms on CPU, GPU, or Neural Engine, analyze performance metrics, and create high-level complexity models.

Nice To Haves

• Master’s or Ph.D. 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 or training environments.
• Experience designing experiments to evaluate ML model optimization techniques.
• Experience creating SoC or IP performance models or simulations.
• Understanding of compiler frameworks and technologies.

Responsibilities

• Create optimized implementations of machine learning workloads on Apple silicon, including the 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 ML techniques and analyze system architecture trade-offs.
• Evaluate emerging ML model optimization techniques through experimentation and analysis, and propose ideas to inform hardware and algorithm direction.
• Prototype algorithms on CPU, GPU, or Neural Engine, analyze performance metrics, and create high-level complexity models.
• Communicate findings and collaborate with partner teams.

What This Job Offers

• Job Type: Full-time