ddn - Senior / Staff NFS Engineer

Remote - California1w ago

Requirements

• Strong hands-on experience with Network File Systems (NFS) • Deep understanding of distributed file systems • Experience working on kernel-level and/or user-space I/O stacks • Familiarity with NVMe, SSDs, RDMA, and high-speed networking • A systems mindset: you know how to debug complex performance and reliability issues across layers • YOU’LL THRIVE HERE IF • You are energized by low-level systems work • You like solving problems most engineers avoid because they are too deep, too subtle, or too performance-sensitive • You care about the details of how storage and networking behave under pressure • You want your work to matter in environments where performance is mission-critical • THIS ROLE IS PROBABLY NOT FOR YOU IF • You’ve used storage systems, but haven’t built or debugged them at a systems level • You prefer abstraction layers over getting hands-on with performance, I/O paths, and infrastructure internals • You want a role focused on coordination more than engineering depth

Responsibilities

• Design, build, and optimize features across DDN’s NFS and storage stack • Diagnose bottlenecks across file systems, storage media, networking, and I/O paths • Improve performance, scalability, and resiliency in distributed storage environments • Work across kernel-space and user-space components to solve hard systems problems • Collaborate with engineers across storage, systems, and platform layers • Develop tooling and automation in Python to improve observability, testing, and operations • Help shape the next generation of infrastructure for AI and data-intensive workloads

Benefits

• At DDN, you will work on problems that sit at the heart of modern infrastructure: • Scaling and optimizing Network File Systems • Building and improving distributed file systems and object storage • Tuning performance across kernel-level and user-space I/O stacks • Working with NVMe, SSDs, RDMA, and high-speed networking • Integrating storage platforms into Kubernetes-native environments • Using Python to automate, debug, test, and improve complex systems • Your work will directly influence how high-performance data platforms behave under real-world load, not just in theory.