Workload Performance Engineer
Cerebras is developing a radically new chip and system to dramatically accelerate deep learning applications. Our system runs training and inference workloads orders of magnitude faster than contemporary machines, fundamentally changing the way ML researchers work and pursue AI innovation.
We are innovating at every level of the stack – from chip, to microcode, to power delivery and cooling, to new algorithms and network architectures at the cutting edge of ML research. Our fully-integrated system delivers unprecedented performance because it is built from the ground up for deep learning workloads.
Cerebras is building a team of exceptional people to work together on big problems. Join us!
You will work with the hardware and software design teams to analyze and optimize workload performance.
- Develop models for the hardware, software stack, and workload to estimate end-to-end performance.
- Develop tools to analyze performance and identify bottlenecks and optimization opportunities.
- Work with design teams to implement optimizations and tune overall performance.
Skills & Qualifications
- PhD or Master’s degree in Computer Science, Electrical Engineering, or equivalent, particularly with focuses in computer architecture
- Experience with performance analysis on CPUs, GPUs, and parallel architectures.
- Experience with end-to-end workload analysis from low level assembly instruction code to high level distributed algorithms.
- Programming/scripting experience in C/C++ and Python
Our cozy and well-appointed headquarters are in the heart of Silicon Valley near downtown Los Altos, California.