Senior Systems Software Engineer - HPC and AI Storage

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.