Staff Linux Kernel Engineer

Crusoe•San Francisco, CA

1d•$215,000 - $260,000

About The Position

Crusoe is seeking a Staff Linux Kernel Engineer to set the technical direction for their Linux kernel team. This role involves owning the roadmap, architecting critical development projects, and balancing high-level architectural design with hands-on kernel coding. The engineer will tackle complex kernel problems, mentor other engineers, and deliver production-ready infrastructure at scale. The company is focused on accelerating energy and intelligence through vertically integrated AI infrastructure, addressing the growing demand for AI compute and power bottlenecks with an energy-first approach.

Requirements

Deep knowledge of Linux kernel internals — memory management, scheduler, networking, storage subsystems and/or device drivers.
Strong C programming skills; fluency in kernel development workflow (patch submission, mailing lists, Lore).
Experience evaluating kernel security vulnerabilities: CVE analysis, exploitability assessment, backporting fixes.
Demonstrated ability to set technical direction and drive projects end-to-end.
Deep knowledge of Linux kernel internals (memory management, scheduler, I/O) and virtualization technologies (KVM, Xen, or QEMU).
10+ years of experience in systems software engineering working on low-level systems or infrastructure teams.

Nice To Haves

Track record of upstream Linux kernel contributions is a strong plus.

Responsibilities

Perform deep-dive root-cause analysis of issues across the OS and hardware layers.
Lead the triage and backporting for kernel CVEs while building automated tooling for vulnerability analysis.
Architect and lead critical projects — new kernel features, security patching infrastructure, deployment systems — from design through production and upstream.
Develop kernel code with an upstream-first mindset, aiming to contribute all changes to the Linux kernel community.
Develop and review kernel code: memory management, scheduler, networking, storage, and GPU-related subsystems.
Design and implement advanced virtualization technologies tailored for GPU-accelerated AI and HPC workloads, optimizing the stack for low latency and high scalability across hypervisors and device emulation.
Find and develop kernel efficiency improvements proactively (e.g., DAMON-based memory management, hugepage optimization, memory overcommit).
Mentor and review work from mid-level and junior engineers.