DDN
Senior / Staff NFS Engineer
RemoteUnited States only
- Role
- Backend
- Experience
- Staff
- Employment
- Full-time
Salary not disclosedModel estimate · $190k–$235k/yrMedium confidence · 80 comparable rolesMedian $205k · Based on role, seniority, location, requirements, employment type, and work arrangement
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No BS summary
Senior or Staff systems engineer with deep NFS, distributed file systems, object storage, Kubernetes, Python, and low-level I/O stack experience. Must be able to debug storage, networking, performance, and reliability issues across kernel/user-space layers. Remote role tied to California.
Core skills
NFSDistributed file systemsPython
Required skills
Object storageKubernetesKernel-level I/O stacksUser-space I/O stacksNVMeSSDsRDMAHigh-speed networking
WHY THIS ROLE IS COMPELLING
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.
WHAT YOU’LL DO
- 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
WHAT WE’RE LOOKING FOR
- Strong hands-on experience with Network File Systems (NFS)
- Deep understanding of distributed file systems
- Experience with object storage
- Production experience with Kubernetes
- Strong Python skills
- 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
- Your background is primarily general backend, SRE, or platform engineering without deep storage/filesystem ownership
- 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
What you'll do
- Scale and optimize Network File Systems
- Build and improve distributed file systems and object storage
- Tune performance across kernel-level and user-space I/O stacks
- Work with NVMe, SSDs, RDMA, and high-speed networking
- Integrate storage platforms into Kubernetes-native environments
- Use Python to automate, debug, test, and improve complex systems
- 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
What they require
- Strong hands-on experience with Network File Systems
- Deep understanding of distributed file systems
- Experience with object storage
- Production experience with Kubernetes
- Strong Python skills
- Experience working on kernel-level and/or user-space I/O stacks
- Familiarity with NVMe, SSDs, RDMA, and high-speed networking
- Systems mindset with ability to debug complex performance and reliability issues across layers
- Energized by low-level systems work
- Interested in solving deep, subtle, performance-sensitive engineering problems
- Care about how storage and networking behave under pressure
- Deep storage/filesystem ownership rather than primarily general backend, SRE, or platform engineering background
- Experience building or debugging storage systems at a systems level
- Preference for hands-on work with performance, I/O paths, and infrastructure internals
- Preference for engineering depth over coordination-focused work
DDN is positioned as NVIDIA’s storage and data intelligence partner for AI factories and the NVIDIA AI Data Platform.
Data Storageddnet.org/
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