Software Engineer, Kernel Reliability

About the role

We're looking for a deeply technical, hands-on software engineer to join our on-field Kernel Reliability team. You'll help tackle a critical challenge: improving the reliability of our advanced compute clusters and the underlying inference, training, and internal production services. In this role, you'll work close to the code and design solutions that will scale with our rapidly growing system production and software service offerings. If you have strong fundamentals in systems, debugging, and failure analysis-and enjoy building tools and solving hard reliability problems-we want to hear from you. New college graduates are welcome.

Responsibilities

• Contribute to the technical roadmap and execution for kernel-centric reliability of our internal and customer-facing systems.
• Partner with System and Cluster Operations teams to reduce system and service downtime after failure through tooling, analysis, and hands-on debugging support.
• Work with the Debug Team to enhance debug tools with the goal of speeding up failure analysis.
• Collaborate with software teams to improve the software stack-including kernels-to improve on-field debugging and failure analysis.
• Work with ASIC and hardware architecture teams to co-design next-generation architectures with reliability and ease of debug in mind.
• Participate in incident response, root-cause analysis, and post-mortems; drive follow-ups that measurably improve reliability over time.

Requirements

• We recognize great engineers come from different backgrounds. If you're excited about the role, we encourage you to apply even if you don't meet every qualification.
• Required (or demonstrated through projects/internships/coursework):
• Strong programming skills in C/C++ and Python.
• Solid foundations in operating systems, computer architecture, and systems programming fundamentals.
• Ability to debug complex issues using logs, traces, and standard debugging workflows; interest in root-cause analysis.
• Exposure to parallel and distributed programming (message passing, multicore, GPU, embedded, etc.).
• Experience building or using debug/diagnostic tools (debuggers, core dump handling, tracing, sanitizers, profilers, etc.).
• Familiarity with debugging distributed and parallel applications (deadlocks, livelocks, race conditions, etc.).
• Knowledge of computer architecture concepts (instruction pipelining, multithreading, networking, memory systems, etc.).
• Operations & Monitoring: familiarity with monitoring, incident response, and post-mortem culture.
• Why Join Cerebras
• Build a breakthrough AI platform beyond the constraints of the GPU.
• Publish and open source their cutting-edge AI research.
• Work on one of the fastest AI supercomputers in the world.
• Enjoy job stability with startup vitality.
• Our simple, non-corporate work culture that respects individual beliefs.
• Read our blog: Five Reasons to Join Cerebras in 2026.
• Apply today and become part of the forefront of groundbreaking advancements in AI!
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Additional Information

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.

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