Senior AI Data Storage Engineer - Cloud-Native, Equity

NVIDIA; North Carolina; On-site; USD 152,000 - 241,500; Full time

Benefits offered by this job

Equity compensation

Benefits

Job summary

NVIDIA is seeking a software engineer to help design and optimize storage and data infrastructure for AI workloads in a hybrid cloud environment. You will work on high-performance storage paths, file systems, and object-store integrations to support exabyte-scale training and inference jobs.

The role emphasizes building observable systems, scalable backends, and reliable operations while collaborating with AI, platform, SRE, and data teams.

Qualifications

• 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, OS, and software design.
• Experience building performance-sensitive storage or cloud-native software.
• Experience with Go, Python, Rust, C/C++, or Java.

Responsibilities

• Build storage technologies, client libraries, and filesystem frameworks for AI workloads.
• Develop high-performance storage paths for training and inference workflows.
• Build observability systems to diagnose storage bottlenecks and expose telemetry.
• Improve performance, scalability, and reliability of storage systems.
• Work with AI teams and platform teams to validate storage against production workloads.
• Use modern software engineering practices with high design and testing standards.

Skills

Algorithms

Distributed systems

OS concepts

Software design

Performance optimization

Education

BS in CS/CE/IS

Tools

Go

Python

Rust

C/C++

Java

Kubernetes

Linux

Job description

NVIDIA is seeking a software engineer to help design and optimize storage and data infrastructure for AI workloads in a hybrid cloud environment. You will work on high-performance storage paths, file systems, and object-store integrations to support exabyte-scale training and inference jobs.

The role emphasizes building observable systems, scalable backends, and reliable operations while collaborating with AI, platform, SRE, and data teams.