$176,000 – $333,500
Listed on NVIDIA’s own careers site. You apply with them directly — we never stand between you and the employer.
What this role is
This role involves designing, deploying, and optimizing large-scale GPU storage systems that power NVIDIA's cloud services, managing everything from architecture to operational automation. It suits engineers with deep experience in distributed storage, Linux systems, and cloud infrastructure who want to own storage performance and reliability across AI/ML workloads at scale.
Our summary, not NVIDIA’s wording. The full posting is on their site.
Skills this role names
- Amazon S3
- Ansible
- Bash (Scripting)
- C++
- Chef
- Elastic Stack (ELK)
- Git
- Go
- Grafana
- InfluxDB
- iSCSI Configuration
- Java
- Kubernetes
- Node.js
- OpenStack
- Prometheus
- Puppet
- Python
- Terraform
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What they ask for
Required
- BS in Computer Science, Storage Systems, or related field, or equivalent practical experience
- 8+ years of hands-on experience in production storage systems
- Experience with distributed and high-performance storage solutions including clustered file systems, parallel file systems, and object storage
- Understanding of block, file, and object storage technologies
- Experience with storage networking protocols
- Proficiency in at least one of: C/C++, Java, Python, Go, Node.js, or Bash
- Experience with infrastructure configuration management tools
- Experience with observability and monitoring tools
- Strong communication and teamwork skills
- Participation in on-call rotation
Nice to have
- Deep expertise in replication strategies and erasure coding
- Experience with capacity planning and performance tuning at scale
- Proficiency with Git, code review, CI/CD, and infrastructure as code
- Strong debugging and systematic problem-solving for storage issues
- Network protocol expertise related to storage performance
- Experience with Kubernetes, OpenStack, or hybrid cloud storage
- Ability to design automated storage migration, backup, and disaster recovery
- Track record of optimizing distributed storage performance at scale