Senior Systems Software Engineer – HPC and AI Storage

🔥 3 minutes ago

🌐 Poland, Germany, +1 more countries – Remote

🇵🇱 Poland – Remote 🇩🇪 Germany – Remote 🇫🇷 France – Remote

💵 zł375k - zł838.5k / year

⏰ Full Time

🟠 Senior

🧑‍💻 Full-stack Engineer

👻 Ghost score 1%

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NVIDIA

WebsiteLinkedInAll Job Openings

10,000+ employees

Founded 1993

🏥 Healthcare

🏭 Manufacturing

🤖 Artificial Intelligence

Healthcare • Manufacturing • Artificial Intelligence

NVIDIA is a leading technology company specializing in accelerated computing and artificial intelligence. NVIDIA pioneers advancements in graphical processing units (GPUs), cloud computing, data centers, and virtual reality, with a focus on gaming, automotive, healthcare, and robotics industries. The company's innovations, such as NVIDIA Omniverse, transform traditional digital processes by enabling high-fidelity simulations and rendering tasks. Their applications span various industries, from autonomous vehicles using NVIDIA DRIVE to healthcare solutions with NVIDIA Clara, and AI-driven analytics and workflows.

📋 Description

• 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

• 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.

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