About the job
Cerebras Systems is at the forefront of AI technology, crafting the world’s largest AI chip – a revolutionary architecture that is 56 times larger than traditional GPUs. Our innovative wafer-scale design delivers unparalleled AI compute power, equivalent to dozens of GPUs, all on a single chip. This unique approach allows us to achieve unmatched training and inference speeds, enabling machine learning practitioners to run extensive ML applications effortlessly, without the complexities of managing numerous GPUs or TPUs.
Our esteemed clientele includes leading model laboratories, global corporations, and pioneering AI-native startups. Recently, OpenAI announced a multi-year partnership with Cerebras, aiming to deploy 750 megawatts of scale, significantly enhancing key workloads with ultra-fast inference.
With our cutting-edge wafer-scale architecture, Cerebras Inference provides the fastest Generative AI inference solution globally, exceeding GPU-based hyperscale cloud inference services by over tenfold. This remarkable increase in speed is redefining the user experience in AI applications, fostering real-time iterations and bolstering intelligence through enhanced computational capabilities.
As a Kernel Optimization Engineer, you will develop high-performance software solutions that bridge the gap between hardware and software, focusing on cutting-edge AI and High-Performance Computing (HPC) workloads. Your primary responsibility will be to implement, optimize, and scale deep learning operations, harnessing the full potential of our custom, massively parallel processor architecture.
You will join a world-class team dedicated to the design, performance tuning, and validation of foundational machine learning and HPC kernels. This role includes developing a library of parallel and distributed algorithms to maximize compute utilization and enhance training efficiency for state-of-the-art AI models. Your contributions will be pivotal in unlocking the complete capabilities of our hardware and accelerating AI innovation.

