ML Systems Performance Engineer

About The Role

Engineers on the inference performance team operate at the intersection of hardware and software, driving end-to-end model inference speed and throughput. Their work spans low-level kernel performance debugging and optimization, system-level performance analysis, performance modeling and estimation, and the development of tooling for performance projection and diagnostics.

Responsibilities

• Build performance models (kernel-level, end-to-end) to estimate the performance of state of the art and customer ML models.
• Optimize and debug our kernel micro code and compiler algorithms to elevate ML model inference speed, throughput and compute utilization on the Cerebras WSE.
• Debug and understand runtime performance on the system and cluster.
• Develop tools and infrastructure to help visualize performance data collected from the Wafer Scale Engine and our compute cluster.

Requirements

• Bachelors / Masters / PhD in Electrical Engineering or Computer Science.
• Strong background in computer architecture.
• Exposure to and understanding of low-level deep learning / LLM math.
• Strong analytical and problem-solving mindset.
• 3+ years of experience in a relevant domain (Computer Architecture, CPU/GPU Performance, Kernel Optimization, HPC).
• Experience working on CPU/GPU simulators.
• Exposure to performance profiling and debug on any system pipeline.
• Comfort with C++ and Python.