Staff/Senior Staff Software Engineer - Cloud Hypervisor R&D

Crusoe's mission is to accelerate the abundance of energy and intelligence. We’re crafting the engine that powers a world where people can create ambitiously with AI — without sacrificing scale, speed, or sustainability.Be a part of the AI revolution with sustainable technology at Crusoe. Here, you'll drive meaningful innovation, make a tangible impact, and join a team that’s setting the pace for responsible, transformative cloud infrastructure.About This Role:The Crusoe Cloud Software Development team is seeking a passionate and experienced Senior Staff Software Engineer specializing in Hypervisor Virtualization and Virtualization Research. This pivotal role is critical in the design, development, and optimization of our virtualization technologies, specifically tailored for an all-AI cloud infrastructure. A deep understanding of hypervisor internals, CPU and memory virtualization, I/O virtualization, and performance optimization is essential for developing reliable, high-performance, and secure virtualized environments that power our cutting-edge AI products. This is a full-time position.What You’ll Be Working On:Hypervisor Development & Optimization: Design, develop, and optimize core hypervisor components (e.g., KVM, QEMU, or custom solutions) to achieve maximum performance and efficiency for AI workloads. This includes focusing on CPU, memory, and I/O virtualization techniques.Virtualization Research & Innovation: Conduct in-depth research into advanced virtualization technologies, exploring novel approaches for isolating and accelerating AI compute, storage, and networking resources. Identify and prototype new virtualization features and enhancements to improve density, throughput, and latency.Virtual Hardware & Device Emulation: Develop and enhance virtual hardware components and device emulation, ensuring optimal performance and compatibility for specialized AI accelerators (e.g., GPUs, DPUs) within the virtualized environment.Performance Analysis & Tuning: Analyze and enhance the performance of the entire virtualization stack, from the hypervisor to the virtualized guest OS, with a specific focus on optimizing for AI/ML workloads. This includes profiling, bottleneck identification, and implementing low-level optimizations.System-Level Troubleshooting: Diagnose and resolve complex system issues within the virtualization layer. Work closely with hardware and guest OS teams to debug and resolve integration challenges.Code Review and Quality Assurance: Conduct thorough code reviews to ensure the highest level of software quality, reliability, and security within the hypervisor and virtualization components.Cross-Functional Collaboration: Collaborate with other engineering teams, including hardware design, OS development, and AI/ML application teams, to ensure cohesive and integrated product development.Technical Leadership: Provide technical guidance and mentorship to junior engineers, fostering a culture of technical excellence and collaborative problem-solving within the virtualization team.What You’ll Bring to the Team:Hypervisor Expertise: Proven deep knowledge of hypervisor internals (e.g., KVM, QEMU, Xen, or other bare-metal hypervisors), including CPU virtualization (VT-x/AMD-V), memory virtualization (EPT/NPT, MMU), and I/O virtualization (SR-IOV, virtio).Virtualization Concepts: Strong understanding of virtual machine lifecycle, live migration, snapshotting, and fault tolerance mechanisms.Linux Kernel Familiarity: Experience with Linux kernel internals as they pertain to virtualization, including device drivers, memory management, and scheduling within a virtualized context.Hardware Understanding: Familiarity with hardware architectures relevant to virtualization, including CPUs (x86, ARM), GPUs, and Smart NICs/DPUs. Experience with hardware offloads and acceleration for virtualization.Performance Optimization: Demonstrated ability to identify and resolve performance bottlenecks in complex virtualized systems. Ex ... (truncated, view full listing at source)