NVIDIA
Senior Software Engineer, Bare Metal Infrastructure – DGX Cloud
$152K–$288K
US, CA, Santa Clara · Senior · Full-time · Posted Oct 28, 2026
- Experience
- 5+ years building production infrastructure software, automation, or services, including substantial hands-on work with bare-metal server systems.
- Education
- BS or MS in Computer Science, Computer Engineering, or equivalent experience.
About the job
Build and maintain software that provisions, validates, monitors, and repairs bare-metal GPU server fleets from hardware introduction through production handoff.
What you'll do
- Develop production services and vendor BMC integrations for server discovery, provisioning, validation, firmware management, auditing, and repair.
- Automate platform and firmware validation, failure detection, recovery, and production handoff; debug issues across hardware, firmware, Linux, and networking.
- Contribute to Rust code and supporting Go and Python tools, using AI agents and LLMs in development and failure investigation.
What they ask for
- BS or MS in Computer Science, Computer Engineering, or equivalent experience.
- 5+ years building production infrastructure software, automation, or services, including substantial hands-on bare-metal server work.
- Strong skills in Rust, Go, or Python; enterprise server hardware knowledge, including BMCs, BIOS/UEFI, and firmware management.
- Direct Redfish and IPMI experience; network boot knowledge; strong Linux and datacenter networking fundamentals; regular use of AI agents and LLM tools in software development.
Nice to have
- Production Rust experience; experience with NVIDIA DGX or NVL72, NVLink, InfiniBand, Spectrum-X, or BlueField DPUs.
- Hardware qualification or rack-scale bring-up experience; Kubernetes, GitOps/Argo CD, or fleet-management platforms; AI-driven tooling with measurable impact.
Pay
152,000 USD - 241,500 USD (base salary, Level 3); 184,000 USD - 287,500 USD (base salary, Level 4)
Tools
Rust, Go, Python, Redfish, IPMI, PXE/iPXE, DHCP, HTTP boot
AI focus
Daily use of AI agents and LLM tools to write, review, and test code and investigate failures, and help shape how the team builds with AI.
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