Negotiable
Undetermined
Remote
Remote
Summary: The Senior Software Engineer role focuses on driving software-hardware co-design for Client AI and data center solutions, with an emphasis on Linux Kernel Memory Management. The position requires expertise in heterogeneous memory, virtualization, and high-bandwidth interconnects for advanced SoC and SSD platforms. Candidates should possess a strong background in memory management, kernel and driver design, and system-level debugging. This role is remote and requires significant experience in the field.
Key Responsibilities:
- Architect and optimize Linux kernel memory management for heterogeneous systems, including UVM, memory tiering, and CXL-based memory expansion.
- Lead the design of Linux device drivers for high-performance interfaces such as PCIe Gen5/6, NVMe, and proprietary AI accelerators.
- Develop and tune KVM and QEMU support for IOMMU, interrupt virtualization, and hardware-assisted memory management.
- Partner with hardware architects to define registers and memory maps for upcoming ARMv9 and RISC-V silicon.
- Resolve critical system bottlenecks and memory corruption issues using advanced tools like Lauterbach TRACE32, hardware emulators, and kernel profilers.
Key Skills:
- MS in Computer Science, Computer Engineering, or a related field.
- Expert knowledge of the Linux MM subsystem (paging, swapping, HugePages, page cache, and LRU eviction policies).
- Deep understanding of PCIe/CXL protocol stacks, cache coherency, and DMA engines.
- Expert proficiency in C and Assembly (ARM/x86); experience with Python for automation and performance modeling.
- Familiarity with hardware security features like TrustZone, ARM CCA, and memory protection units.
Salary (Rate): undetermined
City: undetermined
Country: undetermined
Working Arrangements: remote
IR35 Status: undetermined
Seniority Level: undetermined
Industry: IT
Job Title: Senior Software Engineer Linux Kernel & Device Drivers
job Overview:
As a Senior Engineer in the Systems Software team, you will drive the software-hardware co-design for Client AI and data center solutions. This role requires a visionary approach to Linux Kernel Memory Management, specifically focused on heterogeneous memory, virtualization, and high-bandwidth interconnects for our next-generation SoC and SSD platforms.
Key Responsibilities:
- Memory Management R&D: Architect and optimize Linux kernel memory management for heterogeneous systems, including UVM (Unified Virtual Memory), memory tiering, and CXL-based memory expansion.
- Kernel & Driver Design: Lead the design of Linux device drivers for high-performance interfaces such as PCIe Gen5/6, NVMe, and proprietary AI accelerators.
- Virtualization & Hypervisors: Develop and tune KVM and QEMU support for IOMMU, interrupt virtualization, and hardware-assisted memory management.
- SoC Bring-up & Architecture: Partner with hardware architects to define registers and memory maps for upcoming ARMv9 and RISC-V silicon.
- System-Level Debugging: Resolve critical system bottlenecks and memory corruption issues using advanced tools like Lauterbach TRACE32, hardware emulators (Palladium/Zebu), and kernel profilers.
Technical Skills & Qualifications
- Education: MS in Computer Science, Computer Engineering, or a related field.
- Kernel Deep-Dive: Expert knowledge of the Linux MM subsystem (paging, swapping, HugePages, page cache, and LRU eviction policies).
- Hardware Interface Mastery: Deep understanding of PCIe/CXL protocol stacks, cache coherency (AMBA CHI/ACE), and DMA engines.
- Low-Level Programming: Expert proficiency in C and Assembly (ARM/x86); experience with Python for automation and performance modeling.
- Security & Reliability: Familiarity with hardware security features like TrustZone, ARM CCA, and memory protection units
Preferred Experience:
- Significant contributions to the mainline Linux Kernel (specifically in the
mm/ordrivers/pci/directories). - Experience with Cloud and Data Center workloads and understanding their impact on kernel scheduling and memory latency.
- Exposure to Python , gRPC and REST API would preferred
- Knowledge of Machine Learning frameworks and how they interact with kernel-level memory allocators.