Ml Systems Performance Engineer

Cerebras Systems

Sunnyvale, CA, USA
On-site
Kernel-level performance optimization
System-level performance analysis
Performance modeling and estimation
Cerebras Systems is seeking an ML Systems Performance Engineer to enhance the performance of their AI chip architecture. The role involves optimizing kernel micro code and developing performance models while working at the intersection of hardware and software

Job Summary

  • Engineers on the inference performance team operate at the intersection of hardware and software, driving end-to-end model inference speed and throughput.
  • Optimize and debug our kernel micro code and compiler algorithms to elevate ML model inference speed, throughput and compute utilization on the Cerebras WSE.
  • Develop tools and infrastructure to help visualize performance data collected from the Wafer Scale Engine and our compute cluster.

Matching Summary

Match Score: 85

Cerebras Systems is seeking an ML Systems Performance Engineer to enhance the performance of their AI chip architecture. The role involves optimizing kernel micro code and developing performance models while working at the intersection of hardware and software.

Skills & Requirements

Must-have

  • kernel-level performance optimization
  • system-level performance analysis
  • performance modeling and estimation
  • low-level kernel micro code optimization
  • compiler algorithms optimization
  • runtime performance debugging
  • C++ and Python proficiency

Nice-to-have

  • breakthrough AI platform development
  • cutting-edge AI research
  • fastest AI supercomputers
  • startup vitality with job stability
  • non-corporate work culture

Key Requirements

  • Bachelors / Masters / PhD in Electrical Engineering or Computer Science
  • Strong background in computer architecture
  • Exposure to low-level deep learning / LLM math
  • 3+ years of experience in relevant domain
  • Experience working on CPU/GPU simulators
  • Exposure to performance profiling and debug

Work Rights

Not specified

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