Kernel Driver Software Engineer

About Etched

Etched is building the world’s first AI inference system purpose-built for transformers - delivering over 10x higher performance and dramatically lower cost and latency than a B200. With Etched ASICs, you can build products that would be impossible with GPUs, like real-time video generation models and extremely deep & parallel chain-of-thought reasoning agents. Backed by hundreds of millions from top-tier investors and staffed by leading engineers, Etched is redefining the infrastructure layer for the fastest growing industry in history.

• Kernel-Mode Driver Development: Design, develop, and maintain kernel-mode drivers ensuring high reliability, informative debug, and optimal performance.

• Performance Optimization: Analyze and optimize driver performance for demanding AI workloads, focusing on minimizing latency and maximizing throughput.

• Hardware Integration and Co-Design: Collaborate closely with hardware engineers throughout the ASIC design process..

• Virtualization Support: Implement driver support for device virtualization technologies, including SR-IOV, VFIO, and para-virtualization.

• Memory Management: Implement efficient memory management strategies considering kernel memory mapping, page tables configuration, NUMA awareness for device data caching, and IOMMU configuration.

• Security: Build kernel drivers fundamentally designed to support and maintain security across host processes, physical memory spaces, and device attestation.

• Debugging and Troubleshooting: Diagnose and resolve complex driver-related issues, using common kernel debugging tools and techniques (ftrace, dmesg, etc.) to identify and fix bugs.

• Synchronization and Concurrency: Design and implement synchronization mechanisms to handle concurrent access to multiple accelerators.

• System Validation and Testing: Develop and execute comprehensive test plans to validate driver functionality, stability, and performance in manufacturing and in general production environments.

• Collaboration and Troubleshooting: Collaborate with software and hardware teams to diagnose and resolve complex system-level issues.

• Develop and optimize kernel-mode drivers for new ML accelerators.

• Implement and optimize memory management, including kernel memory mapping and IOMMU configurations, for high-bandwidth data transfers.

• Debug and resolve complex driver-related issues impacting ML workload performance.

• Develop performance benchmarks and profiling tools to analyze driver performance.

• Integrate driver support for advanced features like hardware virtualization and security, including SR-IOV and VFIO.

• Optimizing PCIe communication between the host and PCIe devices, using advanced equipment like PCIe analyzers.

• Implement and debug power management features for PCIe devices.

• Integrating ML accelerators into containerized and virtualized environments.

• Implementing and optimizing para-virtualization techniques for PCIe devices.

• Configure and optimize page tables for efficient memory access from the ML accelerator.

• Participate in hardware-software co-design reviews across teams to optimize performance and power efficiency.

• Proficiency in C/C++.

• Strong understanding of kernel-mode driver development and debugging.

• Deep understanding of operating system internals (Linux preferred).

• Experience with hardware/software interfacing and device drivers.

• Experience with memory management and synchronization in kernel environments.

• Strong understanding of PCIe and other hardware interfaces.

• Experience with device virtualization technologies, including SR-IOV and VFIO.

• Strong understanding of kernel memory mapping, page table configuration, and IOMMU.

• Familiarity with hardware-software co-design principles.

• Proven ability to analyze complex technical problems and provide effective solutions.

• Excellent communication and collaboration 1 skills.

• Experience with version control systems (e.g., Git).

• Experience with debugging tools (e.g., gdb, kgdb).

• Experience with GPU driver development.

• Experience with CUDA, OpenCL, or other GPU programming models.

• Experience with performance profiling and benchmarking tools (perf, VTune).

• Knowledge of hardware virtualization techniques, including para-virtualization.

• Experience with CI/CD pipelines.

• Experience with Rust.

• Experience with ML frameworks like Tensorflow or Pytorch.

• Experience with data center orchestration technologies (Kubernetes, Docker).

• Candidates with experience in developing and debugging kernel-mode drivers for GPU or other accelerator devices.

• Candidates with a strong understanding of hardware/software interactions.

• Candidates with experience in optimizing driver performance for demanding workloads.

• Candidates with experience in ML workloads.

• Candidates who have debugged complex hardware and software interactions, especially in virtualized environments.

• Candidates with experience in implementing and optimizing SR-IOV and VFIO.

• Candidates with…