Senior AI Infrastructure Engineer, Physical Infrastructure

Anduril Industries

Confirmed live yesterday High trust

Quick summary

Work type
On-site
Location
Seattle, WACosta Mesa, CA
Salary
$166,000–$220,000 / yr
Posted
3 days ago
Freshness
Confirmed live yesterday

Market check

Salary context

Competitive pay

How this pay compares to similar roles

Similar $185k
This role $193k
$134k $236k
below market most similar roles pay here above market

This role pays more than 55% of similar roles. Most pay $144,350–$226,350 — the blue band above. At the midpoint, this role pays about $193k versus about $185k for comparable roles.

Based on 240 similar postings.

Employer

About Anduril Industries

Anduril Industries is a defense technology company that builds advanced hardware and software systems for national security, including autonomous drones, surveillance systems, and the Lattice AI command platform.

Anduril Industries currently has 943 open roles on FindRole.

Listed pay typically runs $166,000–$220,000 across 911 roles with salary data.

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At a glance

TL;DR · Senior AI Infrastructure Engineer, Physical Infrastructure

The Senior AI Infrastructure Engineer, Physical Infrastructure joins the CorpTech Infrastructure Engineering team to build and operate the foundational infrastructure powering company-wide AI and autonomy ambitions. This role involves taking ownership of cluster robustness for high-performance GPU systems, ensuring high availability and fault tolerance for ML platform and research teams. You will rack, stack, and cable GPU compute systems like H200 and B200, while building and tuning interconnect fabrics including NVLink, InfiniBand, and RoCE. Key responsibilities include integrating parallel storage such as VAST and Weka, and automating cluster deployments using infrastructure as code. You will operate Kubernetes and Run:AI environments to manage GPU scheduling and multi-tenant workload isolation. The role solves the technical challenge of scaling massive training infrastructure for distributed multi-modal datasets.

What you'll do

  • Rack, stack, cable, and bring up GPU compute systems including physical topology, power, cooling, and firmware.
  • Build and tune interconnect fabrics like NVLink, InfiniBand, and RoCE for low-latency training clusters.
  • Integrate high-performance parallel storage to sustain throughput for distributed training and multi-modal datasets.
  • Automate end-to-end cluster deployment and configuration using infrastructure as code for firmware and fabric management.
  • Operate and extend Kubernetes and Run:AI environments for GPU scheduling, quota management, and workload isolation.
  • Monitor fleet health and perform rapid triage of hardware faults, network congestion, and NCCL failures.
  • Onboard researchers and engineers by debugging and optimizing their workloads when infrastructure becomes a bottleneck.
  • Translate emerging compute needs from product teams into scalable platform capabilities.

What we're looking for

  • 10+ years of hands-on experience in 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 like NVLink, InfiniBand, RoCE, or Spectrum-X in clusters of hundreds of GPUs.
  • Experience with high-performance parallel storage systems such as VAST, DDN, Weka, or Lustre.
  • Proficiency with Kubernetes is required.
  • Strong automation background for building repeatable, automated deployment pipelines.
  • Ability to lift and move 50+ lbs and perform physical datacenter work like racking and cabling.
  • Eligible to obtain and maintain an active U.S. Top Secret clearance.
  • Experience with NVIDIA NVL72 rack scale systems (preferred).
  • Experience supporting LLM token serving/inference infrastructure alongside training clusters (preferred).
  • Network fabric tuning experience for RoCE/InfiniBand at scale (preferred).
  • Familiarity with GPU/network observability tooling and automated fault detection (preferred).
  • Experience supporting infrastructure as a shared platform for multiple internal customer teams (preferred).

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