Anduril
Senior AI Infrastructure Engineer, Physical Infrastructure
$166K–$220K
Seattle, Washington, United States +1 · Senior · Full-time · Posted Oct 8, 2026
- Location
- Seattle, Washington, United States
Costa Mesa, California, United States - Experience
- 10+ years; no alternative stated.
About the job
Leads and operates large-scale GPU training infrastructure, building resilient clusters, tuning networking and storage, and automating deployment and fleet operations.
What you'll do
- Bring up GPU systems and build interconnect and parallel-storage infrastructure for training and inference clusters.
- Automate cluster deployment and operate Kubernetes/Run:AI GPU scheduling, fleet health, and fault response.
- Support researchers and engineers in debugging and optimizing workloads, and translate compute needs into platform capabilities.
What they ask for
- 10+ years in hands-on infrastructure, HPC, or datacenter engineering supporting GPU compute at scale.
- Hands-on experience with H200/B200/B300 or comparable GPU systems, including bring-up, 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 lift/move 50+ lbs and perform physical datacenter work; eligible to obtain and maintain an active U.S. Top Secret clearance.
Nice to have
- Experience with NVIDIA NVL72 rack-scale systems and LLM token serving/inference infrastructure.
- Network fabric tuning, GPU/network observability, automated fault detection, or shared-platform infrastructure experience.
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 GPU model training and inference through resilient compute, networking, storage, scheduling, and automated fault management.
Model-written summary · the original posting is authoritative