Device Driver Engineer
EnCharge AI
| Company | EnCharge AI |
| Category | Engineering |
| Location | US |
| Remote | Remote |
| Employment | Not stated |
| Level | Not stated |
| Salary | Not stated by the employer |
| Posted | 5 May 2026 |
| Last verified | 12 Aug 2026 |
| Source | The employer's own careers page (company_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