Senior ML Compute Efficiency Engineer (GPU/TPU Performance)

Socket.dev; Santa Clara (CA); On-site; USD 130,000 - 180,000; Full time

Job summary

Apple’s Machine Learning Platform Technologies organization seeks a performance engineer to tackle challenges across thousands of GPUs/TPUs, optimize accelerator utilization, and reduce idle capacity while shortening recovery periods. This role focuses on improving efficiency across the ML compute fleet.

You will analyze accelerator performance, explore parallelism techniques, and refine scheduling and orchestration in collaboration with ML research and infrastructure teams.

Qualifications

• Experience with large-scale distributed AI/ML workloads on GPUs/TPUs.
• Strong software engineering skills with experience developing and optimizing training frameworks using C/C++ or Python.
• Experience working on cross-functional projects with ML research and infrastructure teams.
• Familiarity with model architectures and various training techniques.

Responsibilities

• Analyze accelerator performance and identify inefficiencies to maximize utilization.
• Explore parallelism techniques and refine workload scheduling and orchestration across the compute fleet.
• Collaborate with ML research and infrastructure teams to implement performance improvements.

Skills

Distributed systems

C/C++

Python

PyTorch

JAX

Training frameworks

Cross-functional collaboration

Education

Bachelor’s degree in Computer Science or equivalent experience

Job description

Apple’s Machine Learning Platform Technologies organization seeks a performance engineer to tackle challenges across thousands of GPUs/TPUs, optimize accelerator utilization, and reduce idle capacity while shortening recovery periods. This role focuses on improving efficiency across the ML compute fleet.

You will analyze accelerator performance, explore parallelism techniques, and refine scheduling and orchestration in collaboration with ML research and infrastructure teams.