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 run large‑scale ML applications without the hassle of managing hundreds of GPUs or TPUs.

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 increase in speed is transforming the user experience of AI applications, unlocking real‑time iteration and increasing intelligence via additional agentic computation.

As a Kernel Engineer on our team, you will develop high‑performance software solutions at the intersection of hardware and software, creating deep learning kernels that fully leverage our custom, massively parallel processor architecture.

You will be part of a world‑class team responsible for designing, tuning, and validating foundational ML and HPC kernels. This includes building a library of parallel and distributed algorithms that maximize compute utilisation 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.

• Develop design specifications for new machine learning and linear algebra kernels and map them to the Cerebras WSE system using various parallel programming algorithms.
• Develop and debug a kernel library of highly optimised low‑level assembly and CSL routines to implement algorithms targeting the Cerebras hardware system.
• Use mathematical models and analysis to measure 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 and help evolve kernel library architecture to address computational challenges of cutting‑edge neural networks.
• Collaborate with chip and system architects to optimise instruction sets, microarchitecture, and I/O of next‑generation systems.

• Bachelor’s, Master’s, PhD or foreign equivalent in Computer Science, Computer Engineering, Mathematics, or related fields.
• Understanding of hardware architecture concepts – must be comfortable learning details of a new hardware architecture.
• Skilled in C++ and Python programming.
• Strong knowledge of library and API development best practices.
• Strong debugging skills and knowledge of debugging complex software stacks.

• Experience in kernel development and/or testing.
• Familiarity with parallel algorithms and distributed memory systems.
• Experience with programming accelerators such as GPUs and FPGAs.
• Familiarity with machine learning neural networks and frameworks such as Tensor Flow and PyTorch.
• Familiarity with HPC kernels and their optimisation.

• Build a breakthrough AI platform beyond the constraints of the GPU.
• Publish and open‑source cutting‑edge AI research.
• Work on one of the fastest AI supercomputers in the world.
• Enjoy job stability with startup vitality.
• Simple, non‑corporate work culture that respects individual beliefs.

Cerebras Systems is committed to creating an equal and diverse environment and is proud to be an equal opportunity employer. We celebrate different backgrounds, perspectives and skills. We believe inclusive teams build better products and companies. We try every day to build a work environment that empowers people to do their best work through continuous learning, growth and support of those around them.

As set forth in Cerebras Systems’s Equal Employment Opportunity policy, we do not discriminate on the basis of any protected group status under any applicable law.