Characterize, analyze, and optimize performance of state-of-the-art AI models on Cerebras' wafer-scale hardware. Build performance models, optimize kernels and compilers, debug runtime behavior, and develop visualization tools to influence next-gen AI architecture.
Salary not listed
On-site3+ YOEML Engineering
About the role
Responsibilities
Bring up and optimize performance on new generations of the Cerebras WSE.
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.
Skills & 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
Computer ArchitectureC++PythonPerformance ModelingKernel OptimizationHpcCpu/Gpu SimulatorsPerformance ProfilingDeep LearningLlm Math
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