Senior Storage Software Engineer, DGXC Data Services

Details

The NVIDIA DGXC Data Services team builds cloud-native systems, frameworks, and services for managing data across hybrid and multi-cloud infrastructure. The team is developing next-generation data and storage infrastructure for AI workloads, including storage, access, ingestion, governance, observability, and data management for exabyte-scale, high-performance GPU-based training and inference jobs.

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 the 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

• Bachelor's degree in Computer Science, Information Systems, Computer Engineering, or equivalent experience.
• 5 or more 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.

Preferred Qualifications

• 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.

Benefits

• Equity and benefits are provided.
• NVIDIA is committed to fostering an inclusive work environment and is an equal opportunity employer.

Applications for this job will be accepted at least until August 1, 2026.