Kernel Engineer
Cerebras · United States and Canada
CHECKED 8H AGO
Infrastructureonsite
At a glance
Highlights
- Develop high-performance ML kernels for world's largest AI chip, 56x larger than GPUs
- Optimize deep learning ops to unlock 10x faster training and inference speeds.
- Build parallel algorithms maximizing compute on massively parallel WSE architecture.
Ideal candidate
A systems-minded engineer fascinated by hardware-software co-design who loves squeezing performance from low-level code and pushing novel AI hardware to accelerate frontier models.
Requirements
- Bachelor's, Master's, PhD or equivalent in Computer Science, Computer Engineering, Mathematics or related field
- Understanding of hardware architecture concepts and willingness to learn new hardware architecture
- Skilled in C++ and Python programming
- Good knowledge of library and/or API development best practices
- Strong debugging skills for complex software stacks
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