Software Engineer, Kernel Reliability

1 мес. назад

СШАКанадаJunior

kerneldebuggingfailure analysis

Hands-on software engineer role focused on kernel reliability, improving compute cluster reliability and internal production services.

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.
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.

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.

People who are serious about software make their own hardware. At , we have built a breakthrough architecture that is unlocking new opportunities for the AI industry. With dozens of model releases and rapid growth, we’ve reached an inflection point in our business. Members of our team tell us there are five main reasons they joined :
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.
Find out more about what it's like to work at here !
Apply today and become part of the forefront of groundbreaking advancements in AI!
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Systems builds the world's largest AI chip, 56 times larger than GPUs. This architecture allows to deliver industry-leading training and inference speeds; 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. works with the leading model labs, global enterprises, and cutting-edge AI-native startups. OpenAI recently announced a multi-year partnership with , to deploy 750 megawatts of scale, transforming key workloads with ultra high-speed inference.