Senior Storage Software Engineer, DGXC Data Services

NVIDIA’s DGXC Data Services team builds cloud-native data and storage infrastructure for AI workloads across hybrid and multi-cloud environments. The Senior Storage Software Engineer will develop storage technologies, high-performance data access paths, and observability systems, while improving reliability and performance for large-scale 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

Requirements:

• BS in Computer Science, Information Systems, Computer Engineering, or equivalent experience
• 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