Note: The job is a remote job and is open to candidates in USA. NVIDIA is a leader in groundbreaking developments in Artificial Intelligence and High-Performance Computing. They are seeking a Senior Storage Software Engineer to build cloud-native systems and frameworks for managing data across hybrid and multi-cloud infrastructures, focusing on high-performance storage solutions for AI workloads.
Responsibilities
- Build storage technologies, client libraries, and filesystem frameworks that help AI workloads access data across object stores, file systems, and hybrid cloud infrastructure
- Develop high-performance storage paths for training and inference workflows, including data loading, checkpointing, caching, POSIX-style access, and object-store integration
- Build observability systems that diagnose storage bottlenecks, attribute GPU idle time to I/O behavior, and expose actionable telemetry through production monitoring stacks
- Improve performance, scalability, and reliability of storage systems serving massive datasets, deep directory trees, and high-concurrency AI workloads
- Work closely with internal AI teams, platform teams, SRE, and operations to validate storage behavior against real workloads and production environments
- Use modern software engineering practices, including AI-assisted and agentic development workflows, while maintaining high standards for design, testing, security, performance, and verification
Skills
- BS in Computer Science, Information Systems, Computer Engineering, or equivalent experience, with 5+ years of software engineering experience
- Strong foundation in algorithms, data structures, distributed systems, operating systems, and practical software design
- Experience building performance-sensitive systems, storage, backend, or cloud-native software in languages such as Go, Python, Rust, C/C++, or Java
- Experience with storage systems, object stores, caching, Linux systems, Kubernetes, or cloud infrastructure
- Ability to reason about performance, scalability, concurrency, reliability, and operational tradeoffs in production systems
- Ability to design APIs, document systems, communicate clearly, and break ambiguous infrastructure problems into practical execution plans
- Curiosity and practical judgment around AI-assisted or agentic engineering workflows, including using clear intent, specifications, acceptance criteria, tests, and verification to guide development
- Background with Linux kernel observability, eBPF, tracing, or low-overhead telemetry systems
- Experience with FUSE, POSIX filesystems, object-store-backed filesystems, or filesystem metadata/indexing
- Experience optimizing storage performance for AI training, checkpointing, inference, or large-scale data pipelines
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