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Design, build, and optimize features across the NFS and storage stack to improve performance and scalability. Diagnose bottlenecks across file systems, networking, and I/O paths while developing Python automation for observability and testing.
Requires deep hands-on experience with NFS, distributed file systems, and object storage, along with strong Python skills. Candidates must have production experience with Kubernetes and a strong understanding of kernel-level or user-space I/O stacks.
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.
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
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 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
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
The Global Leader in AI and Data Intelligence Solutions.
DDN is the world’s leading AI and data intelligence company, powering the world’s most demanding AI workloads by keeping GPUs fed, efficient, and productive—at massive scale—so organizations can train, checkpoint, and infer faster with less footprint and power while achieving tremendous ROI from their AI investments. From hyperscalers and next-gen cloud builders to enterprises, governments, and research institutions, DDN delivers proven data intelligence at exabyte scale across hundreds of thousands of GPUs—so customers can deploy AI with confidence, accelerate time-to-value, and realize outsized returns.
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