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NVIDIA

Designs the chips and software that power AI

Senior Systems Software Engineer - HPC and AI Storage

Systems Software EngineeringPosted on Nov 9
Full-timePLN375k - PLN838.5kRemote

About the Job

Requirements

  • 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, or Physics, or equivalent practical experience, with 12+ years of relevant experience.

Responsibilities

  • 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, and translate findings into improvements to software, 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.

Desired Qualifications

  • 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.

About the Company

NVIDIA

Designs the chips and software that power AI

NVIDIA designs the chips and software that power artificial intelligence and sells complete data center systems to cloud providers, AI labs, and other businesses, along with GeForce graphics cards for gamers. Its GPUs, chips that perform thousands of calculations at once, became the main hardware for AI after researchers used them to train the AlexNet image-recognition model in 2012. NVIDIA connects many GPUs with its own processors and networking gear into servers and rack-sized computers, and developers program them through its CUDA software. These systems bring in most of its sales, and it also makes chips for cars and robots. Jensen Huang co-founded NVIDIA in 1993, and it is based in Santa Clara, California.

Company size
10,001+
Company stage
IPO
Headquarters
Santa Clara, CA
Founded
1993

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