Anduril
Senior AI Infrastructure Engineer, Physical Infrastructure
$166K–$220K
Costa Mesa, California, United States · Senior · Full-time · Posted Sep 3, 2026
- Experience
- 10+ years; no stated alternative.
About the job
Leads the deployment and operation of large-scale GPU infrastructure for AI training and inference, including physical systems, networking, storage, scheduling, and automated resilience.
What you'll do
- Rack, configure, connect, and validate GPU systems, interconnects, and parallel storage.
- Automate cluster deployment and operate GPU scheduling, fleet health monitoring, and fault response.
- Support internal research and engineering teams in debugging and optimizing infrastructure-bound workloads.
What they ask for
- 10+ years in hands-on infrastructure, HPC, or datacenter engineering supporting GPU compute at scale.
- Hands-on experience bringing up comparable GPU systems, including cabling and firmware/driver management.
- Experience with high-performance interconnects in clusters of hundreds of GPUs and high-performance parallel storage.
- Kubernetes; strong automation background; able to perform physical datacenter work and lift/move 50+ lbs; eligible to obtain and maintain an active U.S. Top Secret clearance.
Nice to have
- Run:ai or similar GPU scheduling/orchestration experience.
- Experience with NVIDIA NVL72, LLM inference infrastructure, fabric tuning, GPU/network observability, or shared platforms.
Pay
$166,000 — $220,000 USD (base salary)
Tools
H200/B200/B300, NVL72, NVLink, InfiniBand, RoCE, Spectrum-X, VAST, DDN
AI focus
Enables large-scale AI model training and inference by building and operating GPU clusters, scheduling, networking, storage, and automated resilience.
Model-written summary · the original posting is authoritative