Kernel Engineer

Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. Our novel wafer-scale architecture provides the AI compute power of dozens of GPUs on a single chip, with the programming simplicity of a single device. This approach allows Cerebras to deliver industry-leading training and inference speeds and empowers machine learning users to effortlessly run large-scale ML applications, without the hassle of managing hundreds of GPUs or TPUs. Cerebras' current customers include global corporations across multiple industries, national labs, and top-tier healthcare systems. In January, we announced a multi-year, multi-million-dollar partnership with Mayo Clinic, underscoring our commitment to transforming AI applications across various fields. In August, we launched Cerebras Inference, the fastest Generative AI inference solution in the world, over 10 times faster than GPU-based hyperscale cloud inference services. About The Role As a Kernel Engineer on our team, you will work with leaders from industry and academia at the intersection of hardware and software to develop state-of-the-art solutions for emerging problems in AI and HPC. Our team of developers is responsible for the design, implementation, validation, and performance tuning of deep learning operations on highly parallel custom processors. We are developing a library of parallel and distributed algorithms to maximize hardware utilization and accelerate the training of deep neural networks to unprecedented speeds. Responsibilities 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. 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. Interact with chip and system architects to optimize instruction sets, microarchitecture, and IO of next generation systems

Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. Our novel wafer-scale architecture provides the AI compute power of dozens of GPUs on a single chip, with the programming simplicity of a single device. This approach allows Cerebras to deliver industry-leading training and inference speeds and empowers machine learning users to effortlessly run large-scale ML applications, without the hassle of managing hundreds of GPUs or TPUs.