Senior staff software engineer

Senior Staff Software Engineer - Hypervisor Virtualization and Virtualization Research Bangalore / Hyderabad Founded by highly respected Silicon Valley veterans - with its design centers established in Santa Clara, California. / Hyderabad/ Bangalore Our pay comprehensively beats " ALL" Semiconductor product players in the Indian market. The 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, GPU 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/GPU , memory, and I/O virtualization techniques. Multi-Tenant Acceleration: Optimize secure, high-performance GPU sharing using technologies like MIG and v GPU. Enable concurrent, isolated multi-tenant execution with predictable Qo S and minimal overhead, leveraging hardware partitioning for near bare-metal training and inference performance. Security with Confidential Computing: Enhance hypervisor security through hardware-assisted isolation, secure GPU device assignment, and attack surface hardening. Integrate confidential computing extensions (e.g., AMD SEV-SNP, Intel TDX) to protect AI workloads against hypervisor escapes, DMA attacks, and side-channel threats in multi-tenant environments. 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). Implement and optimize GPU virtualization strategies (e.g., SR-IOV, mediated passthrough) to enable multi-tenant access to accelerators without compromising performance or isolation. 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. Experience with profiling tools and techniques. Debugging & Troubleshooting: Strong debugging skills in complex, distributed systems at the hypervisor and kernel levels. Experience-10-15 years Bonus Points: Experience with virtualization specifically for AI/ML workloads, including GPU virtualization or direct pass-through. Familiarity with container runtimes and their interaction with hypervisors. Contributions to open-source virtualization projects. Experience with security hardening of hypervisors and virtual machines. Contact: Uday Bhaskar Mulya Technologies " Mining the Knowledge Community" Email id :