NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It’s a unique legacy of innovation that’s fueled by great technology—and amazing people. Today, we’re tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As an NVIDIAN, you’ll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join the team and see how you can make a lasting impact on the world.
We are hiring a Senior Systems Software Engineer to join our Applied Systems Engineering team and improve storage development in AI systems. This opportunity involves software development, practical experimentation, and systems integration to refine storage on next-generation NVIDIA platforms. You will lead technical analyses, transform workload insights into engineering fixes, and guide the formulation and implementation of storage policies. You will handle storage integration, testing, and rollout while coordinating with engineering groups on hardware and software projects. This role supports the efficiency and dependability encouraged at datacenter scale.
What you'll be doing:
- Engineer and integrate storage systems for large-scale HPC and AI clusters, including Lustre, NFS, and their interaction with compute nodes, networks, and storage hardware.
- Examine performance and reliability issues throughout the I/O stack, supervising application behavior across filesystem clients, Linux kernel paths, network protocols, servers, and storage devices.
- Develop software fixes and performance improvements, working with NVIDIA engineering teams, storage partners, and open-source maintainers to bring changes through review, testing, and deployment.
- Characterize real application I/O, including training-data access, checkpoint and restart, metadata-intensive operations, and concurrent workloads. Translate findings into improvements to software, and system configuration and design.
- Build reproducible benchmarks, diagnostic tools, and regression tests that measure efficiency, latency, metadata performance, scalability, and behavior under contention.
- Lead storage qualification and commissioning, validating functionality, data integrity, failure recovery, and sustained performance at cluster scale.
- Define and implement storage placement, caching, and data lifecycle policies informed by workload requirements, capacity, and operational evidence.
What we need to see:
- Deep engineering experience with Lustre, NFS, or comparable shared storage systems, including implementation-level debugging in at least one stack and experience integrating storage into Linux clusters.
- Strong C programming and Linux debugging skills, with experience reading, modifying, and diagnosing kernel or user-space storage software.
- Understanding of filesystem and distributed-system behavior, including metadata operations, caching and consistency, locking, concurrency, client/server communication, and recovery.
- Experience analyzing application access patterns and tuning filesystem or storage-stack behavior to deliver performance improvements.
- Ability to take a complex performance or correctness problem from initial symptoms through reproduction, root-cause analysis, implementation, and validation.
- Proficiency in Python and shell scripting to build maintainable diagnostic, test automation, and analysis tools.
- Ability to independently lead technical efforts, influence design decisions across teams, and communicate findings and tradeoffs clearly.
- BS or MS in Computer Science, Engineering, Mathematics, Physics, or equivalent practical experience, with 12+ years of relevant experience.
Ways to stand out from the crowd:
- Contributions to Lustre, Linux filesystems, NFS implementations, or other storage projects, including features, performance improvements, bug fixes, or technical reviews.
- Expertise in Lustre metadata and client I/O, Linux VFS and page cache, NFS protocol and client/server internals, or distributed locking and recovery.
- Experience with hierarchical storage management, policy engines, or migration between filesystems and object storage, including data recall and recovery from interrupted transfers.
- Experience with RDMA storage transports, GPUDirect Storage, or optimizing data loading and checkpointing for GPU workloads.
- Experience with storage security and multi-tenancy, including authentication, identity mapping, permissions, and encryption.
Poland: 375,000 PLN – 650,000 PLN for Level 5; and 483,750 PLN – 838,500 PLN for Level 6.
France: 114,000 EUR – 197,600 EUR for Level 5; and 150,000 EUR – 260,000 EUR for Level 6.
Germany: 141,900 EUR – 245,960 EUR for Level 5; and 165,825 EUR – 287,430 EUR for Level 6.
Skills Required
- Deep engineering experience with Lustre, NFS, or comparable shared storage systems
- Implementation-level debugging in at least one storage stack
- Experience integrating storage into Linux clusters
- Strong C programming skills
- Strong Linux debugging skills
- Experience reading, modifying, and diagnosing kernel or user-space storage software
- Understanding of filesystem and distributed-system behavior, including metadata operations, caching and consistency, locking, concurrency, client/server communication, and recovery
- Experience analyzing application access patterns and tuning filesystem or storage-stack behavior
- Ability to lead performance or correctness problems through reproduction, root-cause analysis, implementation, and validation
- Proficiency in Python and shell scripting
- Ability to independently lead technical efforts and influence cross-team design decisions
- Clear communication of technical findings and tradeoffs
- BS or MS in Computer Science, Engineering, Mathematics, Physics, or equivalent practical experience
- 12+ years of relevant experience
- Contributions to Lustre, Linux filesystems, NFS implementations, or other storage projects
- Expertise in Lustre metadata and client I/O, Linux VFS and page cache, NFS internals, or distributed locking and recovery
- Experience with hierarchical storage management, policy engines, or migration between filesystems and object storage
- Experience with RDMA storage transports, GPUDirect Storage, or GPU workload data loading and checkpointing
- Experience with storage security and multi-tenancy
NVIDIA Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.
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Equity Value & Accessibility — Feedback suggests equity grants, stock options/RSUs, and a discounted ESPP materially boost total compensation and can deliver significant wealth-building when the stock performs well.
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Healthcare Strength — Feedback suggests medical, dental, and vision plans with in-network preventive care at 100%, telehealth, and concierge-style support are comprehensive and valued.
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Parental & Family Support — Feedback suggests up to 22 weeks fully paid parental leave, fertility coverage, and adoption/surrogacy reimbursement provide strong family-focused benefits.
NVIDIA Insights
What We Do
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”









