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Device Driver Engineer

EnCharge AI
CompanyEnCharge AI
CategoryEngineering
LocationUS
RemoteRemote
EmploymentNot stated
LevelNot stated
SalaryNot stated by the employer
Posted5 May 2026
Last verified12 Aug 2026
SourceThe employer's own careers page (company_site)
Applications are handled by the employer, not by us.Apply on the employer's site →
Description
EnCharge AI is a leader in advanced AI hardware and software systems for edge-to-cloud computing. This role involves designing and implementing high-performance device driver stacks for cutting-edge AI accelerator hardware, working closely with hardware, firmware, and software teams to enable low-latency, high-bandwidth communication between host systems and AI accelerators. What You'll Do • Develop, optimize, and maintain Linux and Windows PCIe device drivers for AI accelerators • Implement low-level hardware interactions including DMA, memory management, and interrupt handling • Optimize drivers to reduce latency and improve throughput for AI workloads • Debug and troubleshoot PCIe protocol issues, kernel panics, crashes, and performance bottlenecks • Ensure compliance with PCIe standards (Gen4/Gen5), SR-IOV, BAR memory mapping, and IOMMU • Support virtualization technologies (VFIO, SR-IOV, DPUs) and containerized environments • Develop tools for profiling, debugging, and monitoring driver performance • Collaborate with hardware, firmware, and AI software teams to define driver interfaces What You Need • Bachelor's or Master's degree in Computer Science, Electrical Engineering, or related field • 3+ years of experience in device driver development for Linux and/or Windows • Strong experience with PCIe-based hardware including BAR regions, DMA, interrupts, and MMIO • Proficiency in C/C++ and kernel-mode programming (Linux Kernel, Windows WDDM/WDF/MCDM) • Knowledge of low-level debugging tools (gdb, perf, ftrace, dmesg, PCIe analyzers) • Understanding of multi-threading, synchronization, and memory management in kernel space Nice to Have • Experience with AI-specific accelerators (GPUs, NPUs, TPUs) • Familiarity with high-performance AI/ML workloads • Experience in hypervisor interactions, VFIO, and passthrough solutions • Knowledge of secure boot, firmware updates, and trusted execution environments (TEE) • Contribution to open-source kernel modules