Staff Kernel Optimzation 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 top model labs, global enterprises, and cutting-edge AI-native startups. OpenAI recently announced a multi-year partnership with Cerebras , to deploy 750 megawatts of scale, transforming key workloads with ultra high-speed inference. Thanks to the groundbreaking wafer-scale architecture, Cerebras Inference offers the fastest Generative AI inference solution in the world, over 10 times faster than GPU-based hyperscale cloud inference services. This order of magnitude increase in speed is transforming the user experience of AI applications, unlocking real-time iteration and increasing intelligence via additional agentic computation. About The Role As a Kernel Engineer on our team, you will develop high-performance software solutions at the intersection of hardware and software, developing high-performance software for cutting-edge AI and HPC workloads. Your focus will be on implementing, optimizing, and scaling deep learning operations to fully leverage our custom, massively parallel processor architecture. You will be part of a world-class team responsible for the design, performance tuning, and validation of foundational ML and HPC kernels. This includes building a library of parallel and distributed algorithms that maximize compute utilization and push the boundaries of training efficiency for state-of-the-art AI models. Your work will be critical to unlocking the full potential of our hardware and accelerating the pace of AI innovation. 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. 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. Interact with chip and system architects to optimize instruction sets, microarchitecture, and IO of next generation systems. Skills And Qualifications Bachelor’s, Master’s, PhD or foreign equivalents in Computer Science, Computer Engineering, Mathematics, or related fields. Understanding of hardware architecture concepts — must be comfortable learning the details of a new hardware architecture. Skilled in C++ and Python programming languages. Good knowledge of library and/or API development best practices. Strong debugging skills and knowledge of debugging complex software stack. Preferred Skills And Qualifications Experience in kernel development and/or testing. Familiarity with parallel algorithms and distributed memory systems. Experience in programming accelerators such as GPUs and FPGAs. Familiarity with Machine Learning neural networks and frameworks such as TensorFlow and PyTorch. Familiarity with HPC kernels and their optimization. Why Join Cerebras People who are serious about software make their own hardware. At Cerebras we have built a breakthroug