Senior / Staff NFS Engineer

DDN; United States; Teletrabalho; USD 140 000 - 190 000; Tempo integral

Resumo da oferta

DDN seeks a systems-focused engineer to design, build, and optimize features across its NFS and storage stack. You will diagnose bottlenecks in file systems, storage media, networking, and I/O paths, and push for higher performance, scalability, and resiliency in distributed environments.

You’ll work across kernel-space and user-space components and collaborate with storage, systems, and platform engineers while developing Python tooling to improve observability, testing, and operations for AI

Qualificações

• Strong hands-on experience with NFS.
• Deep understanding of distributed file systems.
• Experience with object storage.
• Production experience with Kubernetes.
• Strong Python skills.
• Experience working on kernel-space and/or user-space I/O stacks.
• Familiarity with NVMe, SSDs, RDMA, and high-speed networking.
• A systems mindset: debugging complex performance issues across layers.

Responsabilidades

• 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.

Conhecimentos

NFS experience

Distributed file systems

Performance debugging

Systems mindset

Kernel/user-space I/O stacks

Ferramentas

Python

Kubernetes

NVMe

RDMA

SSDs

Descrição da oferta de emprego

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