ML Platform Architect — Silicon Performance

Apple, Inc.; Cupertino (CA); In loco; USD 150.000 - 225.000; Tempo pieno

Vantaggi offerti da questo lavoro

Medical and dental coverage

Retirement benefits

Employee stock purchase plan (ESPP)

Tuition reimbursement

Relocation assistance

Discounted products and free services

Descrizione del lavoro

Apple is seeking a Machine Learning Engineer for Platform Architecture in Cupertino. You will map ML workloads to Apple silicon, build performance models and simulations, and inform architecture decisions. You’ll collaborate with IP/SoC teams to optimize ML workloads on Neural Engine, GPU, and CPU.

The role focuses on exploring ML techniques, validating system-level performance, and communicating findings to software and algorithm teams. A strong background in ML and hardware is required.

Competenze

• Programming experience in C/C++ and/or Python.
• Experience in computer architecture.
• Experience with at least one hardware IP: ML HW accelerators or processing units such as GPU, image/video, CPUs, or similar.

Mansioni

• Create optimized implementations of ML 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.
• 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; propose new ideas to inform hardware and algorithm direction.

Conoscenze

C/C++

Python

Computer architecture

Hardware IP

Formazione

MS or PhD in EE/CE/CS or related field

Descrizione del lavoro

Apple is seeking a Machine Learning Engineer for Platform Architecture in Cupertino. You will map ML workloads to Apple silicon, build performance models and simulations, and inform architecture decisions. You’ll collaborate with IP/SoC teams to optimize ML workloads on Neural Engine, GPU, and CPU.

The role focuses on exploring ML techniques, validating system-level performance, and communicating findings to software and algorithm teams. A strong background in ML and hardware is required.