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ML Systems Performance Engineer

Cerebras· Software Engineering
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United States and CanadaFull-timePosted 3mo ago

About the role

Engineers on the inference performance team optimize end-to-end model inference speed and throughput at the intersection of hardware and software, spanning kernel performance, system analysis, performance modeling, and 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.

Qualifications

  • 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.

Skills

Required:Kernel optimizationPerformance modelingSystem performance analysisC++Python
Last updated Aug 30, 2026. Interested? Apply on the company site.

About Cerebras

Cerebras is the go-to platform for fast and effortless AI training. Learn more at cerebras.ai.

Cerebras is the go-to platform for fast and effortless AI training.

AICerebras Wafer-Scale Enginechipshardwareinferenceliquid-ai-recruiting-targetmultimodalsetter-30
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