onsite
ML Systems Performance Engineer
ML Systems Performance Engineer
The ML Systems Performance Engineer will work on the inference performance team, focusing on optimizing end-to-end model inference speed and throughput. This involves developing performance models, debugging kernel micro code, analyzing system-level performance, and creating tools for performance diagnostics.
About the role
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.
Skills
Computer Architecturelow level deep learningLLM mathanalyticalproblem solvingCPU/GPU PerformanceKernel OptimizationHpcCPU/GPU simulatorsperformance profilingC++Python