Senior Storage Software Engineer, DGXC Data Services

NVIDIA•Santa Clara, WA

2d•$152,000 - $287,500

About The Position

The NVIDIA DGXC Data Services team builds cloud-native systems, frameworks, and services for managing data across hybrid and multi-cloud infrastructure. We are building the next-generation data and storage infrastructure to solve some of the hardest problems in AI: storage, access, ingestion, governance, observability, and data management for exabyte-scale, high-performance GPU-based training and inference jobs. Our work gives NVIDIA teams the foundational capabilities they need to build, train, deploy, and operate AI products at scale without reinventing critical data infrastructure for every workload.

Requirements

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.

Nice To Haves

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.

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.