ML Systems Performance Engineer
Cerebras · United States and Canada
CHECKED 8H AGO
Infrastructureonsite
At a glance
Highlights
- Drive end-to-end inference speed on the world's largest AI chip.
- Build performance models to project speed of cutting-edge ML models.
- Visualize performance data from Wafer Scale Engine and compute cluster.
Ideal candidate
Analytical systems thinker fascinated by hardware-software interplay who loves digging into low-level performance to make AI models run dramatically faster.
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.
Lab profile
About Cerebras
Builds wafer-scale AI chips and cloud systems for ultra-fast training and inference.
What they build
- Wafer-scale AI chips
- AI training systems
- Cloud inference service
Known for
- 56x larger chips than GPUs
- 10x faster inference than GPU clouds
- Industry-leading training speed