The opportunity
Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. This architecture allows Cerebras to deliver industry-leading training and inference speeds; over 10 times faster than GPU-based hyperscale cloud inference services.
What you'll do
Develop design specifications for new machine learning and linear algebra: kernels and mapping to the Cerebras WSE System using various parallel programming algorithms.
Develop and debug kernel library of highly optimized low level assembly: instruction and C-like domain specific language routines to implement algorithms targeting the Cerebras hardware system.
Develop and debug high-performance kernel routines in low-level assembly and: a custom C-like (CSL) language, implementing algorithms optimized for the Cerebras hardware system.
Using mathematical models and analysis to measure the software performance and inform design decisions.
Develop and integrate unit and system testing methodologies to verify correct: functionality and performance of kernel libraries.
Study emerging trends in Machine Learning applications and help evolve Kernel: library architecture to address computational challenges of the start-of-the-art Neural Networks.
What they're looking for
- Interact with chip and system architects to optimize instruction sets,: microarchitecture, and IO of next generation systems.
- Bachelor’s, Master’s, PhD, or foreign equivalent in Computer Science,: Computer Engineering, Mathematics, or a related field.
- Proven experience leading technical teams, including mentoring engineers,: setting technical direction, and driving execution.
- Strong understanding of hardware architecture concepts and willingness to dive into new system architectures.
