ML Systems Performance Engineer
Cerebras · Bengaluru, IND
CHECKED 6H AGO
Infrastructureonsite
At a glance
Highlights
- Drive end-to-end inference speed on the world's largest AI chip.
- Optimize kernel microcode to boost model throughput and utilization.
- Build tools to visualize performance across wafer-scale clusters.
Ideal candidate
Analytical systems thinker with computer architecture depth who loves low-level performance debugging and operating at the hardware-software boundary to push AI inference speed.
Requirements
- Bachelor's / Master's / PhD in Electrical Engineering or Computer Science
- Strong background in computer architecture
- Understanding of low-level deep learning / LLM math
- 3+ years in Computer Architecture, CPU/GPU Performance, Kernel Optimization or HPC
- Experience working on CPU/GPU simulators
- Exposure to performance profiling and debug
- Comfort with C++ and Python
- Strong analytical and problem-solving mindset
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