System Software Engineer (Linux Kernel & Device Drivers)

Location: Mountain View, California, United States; onsite

Salary: $200,000–$350,000 per year, with other compensation ranges and equity mentioned in the source.

Company

Early-stage AI hardware company building high-performance accelerator systems for large-scale AI workloads.

What you will do

• Design and develop Linux kernel drivers for PCIe-connected accelerator hardware.
• Own DMA, memory management, interrupts, MMIO, command submission, and device initialization.
• Build low-level userspace libraries and interfaces using ioctl, sysfs, mmap, and direct device access.
• Develop prototype drivers, debugging utilities, bring-up tools, and regression infrastructure.
• Validate software on simulation, emulation, and FPGA-based pre-silicon platforms.
• Work with hardware and firmware engineers on first-silicon bring-up, telemetry, platform integration, and performance optimization.

Requirements

• 8+ years of experience in low-level systems software, kernel development, firmware, or related work.
• Strong hands-on experience developing Linux kernel device drivers.
• Excellent C programming skills and experience working in both kernel space and userspace.
• Ability to work from hardware documentation, register maps, and device specifications.
• Strong understanding of PCIe, DMA, MMIO, interrupts, IOMMU, virtual memory, memory hierarchy, and operating-system internals.
• Experience debugging complex hardware/software interaction issues in an evolving early-stage environment.

Nice to have

• Experience with accelerator, GPU, NIC, storage-controller, or SoC device drivers.
• First-silicon or new-platform bring-up and pre-silicon simulation, emulation, or FPGA environments.
• Experience with SR-IOV, VFIO, dma-buf, PCIe peer-to-peer, perf, ftrace, or eBPF.
• Experience with BMC/OpenBMC, I2C, SMBus, or other sideband management interfaces.
• Systems programming in Rust.

Culture & Benefits

• Hands-on ownership within a small, highly technical team.
• Direct exposure to cutting-edge AI silicon from early development through production.
• Close collaboration across hardware, firmware, kernel, and platform software.