Software Engineer, Kernel Reliability
Salary not listed • United States and Canada • Full-time
Posted 1mo ago
No salary is listed. Many US states require employers to post a pay range, so ask about pay early.
About the job
We are seeking a highly technical, hands-on software engineer to join our Kernel Reliability team. This role focuses on improving the reliability of our advanced compute clusters and the underlying inference, training, and internal production services. You will work closely with the code, designing scalable solutions for 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 complex reliability problems, we encourage you to apply. New college graduates are welcome.
Responsibilities
- Contribute to the technical roadmap and execution for kernel-centric reliability of 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.
- Enhance debug tools with the goal of speeding up failure analysis.
- Collaborate with software teams to improve the software stack, including kernels, for better 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, driving follow-ups to measurably improve reliability over time.
Requirements
- 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.).
- Familiarity with monitoring, incident response, and post-mortem culture.
Benefits
- 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.
- A simple, non-corporate work culture that respects individual beliefs.