Software Engineer (Kernel Reliability)

Location: United States and Canada

Company

Builds large-scale AI compute hardware and software infrastructure designed to accelerate model training and inference beyond GPU-based systems.

What you will do

• Contribute to the technical roadmap for kernel-centric reliability across internal and customer-facing systems.
• Develop tooling and provide hands-on debugging support to reduce downtime after system and service failures.
• Enhance diagnostic and debug tools to accelerate failure analysis.
• Collaborate with software, ASIC, and hardware architecture teams on reliable, debuggable systems and next-generation architectures.
• Participate in incident response, root-cause analysis, and post-mortems, driving follow-up improvements.

Requirements

• Strong programming skills in C/C++ and Python.
• Solid foundations in operating systems, computer architecture, and systems programming.
• Ability to debug complex issues using logs, traces, and standard debugging workflows.
• Interest in root-cause analysis; relevant skills may be demonstrated through projects, internships, or coursework.
• New college graduates are welcome.

Nice to have

• Exposure to parallel or distributed programming, including message passing, multicore, GPU, or embedded systems.
• Experience with debuggers, core dumps, tracing, sanitizers, profilers, or other diagnostic tools.
• Familiarity with deadlocks, livelocks, race conditions, and debugging parallel applications.
• Knowledge of instruction pipelining, multithreading, networking, and memory systems.
• Familiarity with monitoring, incident response, and post-mortem practices.

Culture & Benefits

• Work on AI hardware and software infrastructure designed to overcome GPU limitations.
• Opportunity to contribute to open-source AI research and work with high-performance AI systems.
• Startup vitality combined with job stability.
• Non-corporate culture that respects individual beliefs and supports continuous learning and growth.