Senior Systems Software Engineer – HPC and AI Storage

NVIDIA

. 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.

Posted 11/9/2026full-timeRemote • Poland, Germany, FranceSenior💰 PLN 375,000 - PLN 838,500 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in engineering and integrating storage systems for HPC and AI clusters, with a strong focus on Lustre and NFS. Proficient in performance analysis, debugging, and developing software solutions to enhance storage efficiency and reliability.

Highest-signal resume keywords

Lustre Storage SystemsNFS Storage SystemsC ProgrammingLinux DebuggingPython Scripting

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Hard Skills

Storage IntegrationFilesystem Behavior AnalysisPerformance TuningDiagnostic Tool DevelopmentRoot-Cause AnalysisMetadata OperationsConcurrency ManagementData Lifecycle PoliciesHierarchical Storage ManagementRDMA Storage Transports

Soft Skills

Technical LeadershipCross-Team CommunicationInfluencing Design Decisions

Tools & Technologies

Linux ClustersDiagnostic ToolsTest AutomationOpen-Source CollaborationGPUDirect Storage

Industry Keywords

High-Performance ComputingArtificial IntelligenceData IntegrityFailure RecoveryMulti-Tenancy Security

Tech Stack

Tools & technologies

LinuxNFSPythonShell Scripting

About the role

Key responsibilities & impact

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 with NVIDIA engineering teams, storage partners, and open-source maintainers through review, testing, and deployment.
Characterize application I/O, including training-data access, checkpoint and restart, metadata-intensive operations, and concurrent workloads; translate findings into software, configuration, and design improvements.
Build reproducible benchmarks, diagnostic tools, and regression tests measuring efficiency, latency, metadata performance, scalability, and contention behavior.
Lead storage qualification and commissioning, validating functionality, data integrity, failure recovery, and sustained cluster-scale performance.
Define and implement storage placement, caching, and data lifecycle policies based on workload requirements, capacity, and operational evidence.

Requirements

What you’ll need

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.
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.