Note: The job is a remote job and is open to candidates in USA. Dice is seeking a Senior Engineer in the Systems Software team to drive software-hardware co-design for Samsung’s AI and data center solutions. The role focuses on Linux Kernel Memory Management, particularly for heterogeneous memory and high-bandwidth interconnects for next-generation SoC and SSD platforms.

Responsibilities

• Architect and optimize Linux kernel memory management for heterogeneous systems, including UVM (Unified Virtual Memory), 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 (Palladium/Zebu), and kernel profilers

Skills

• Memory Architecture
• C/C++ Programming
• Low-Level Design
• Embedded OS (Linux/RTOS)
• System-Level Programming
• Experience working in ambiguous and fast-changing environments
• 5 to 15 years of experience (depending on grade)
• Strong kernel development experience and C++
• Low level design experience
• Exposure to python, gRPC and REST API
• 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 (AMBA CHI/ACE), and DMA engines
• Expert proficiency in C and Assembly (ARM/x86)
• Familiarity with hardware security features like TrustZone, ARM CCA, and memory protection units
• Significant contributions to the mainline Linux Kernel (specifically in the `mm/` or `drivers/pci/` directories)
• Experience with Cloud and Data Center workloads and understanding their impact on kernel scheduling and memory latency
• Knowledge of Machine Learning frameworks and how they interact with kernel-level memory allocators

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