Senior AI Infra Engineer: Large-Scale GPU & HPC

Anduril Industries; Costa Mesa (CA); On-site; USD 166,000 - 220,000; Full time

Benefits offered by this job

Equity grants

Benefits package

Job summary

Anduril Industries is seeking a Senior AI Infrastructure Engineer to own the stability and expansion of GPU training infrastructure. You will lead deployment, tuning, and automation across Kubernetes, Run:AI, Ray, and high-speed interconnects to support ML research and production workloads.

This hands-on role requires deep experience with GPU systems (H200/H300), large-scale cluster design, and sustaining uptime while enabling fast, reliable model training and inference.

Qualifications

• 10+ years in a hands on infrastructure, HPC, or datacenter engineering role supporting GPU compute at scale.
• Hands on experience with H200/B200/B300 (or comparable) GPU systems: bring up, cabling, firmware/driver management.
• Experience with high performance interconnects (NVLink, InfiniBand, RoCE, Spectrum-X) in clusters of hundreds of GPUs.
• Experience with high performance parallel storage (VAST, DDN, Weka, Lustre, or similar).
• Kubernetes required; Run:ai or similar GPU scheduling/orchestration experience strongly preferred.
• Strong automation background. You build repeatable, automated deployment pipelines rather than manual processes.
• Able to lift/move 50+ lbs and perform physical datacenter work (rack/stack/cable/troubleshoot).
• Eligible to obtain and maintain an active U.S. Top Secret clearance.

Responsibilities

• Rack, stack, cable, and bring up GPU compute (H200/B200/B300, NVL72) including physical topology, power, cooling, firmware/BIOS, and burn in validation.
• Build and tune the interconnect fabric (NVLink, InfiniBand, RoCE, Spectrum-X) connecting hundreds of GPUs into low latency training and inference clusters.
• Integrate high performance parallel storage (VAST, DDN, Weka) to sustain the throughput demanded by distributed training and terabyte scale multi modal datasets across Anduril’s programs.
• Automate cluster deployment and configuration end to end, including infrastructure as code for bring up, firmware/driver management, and fabric config, so new capacity comes online with minimal manual work.
• Operate and extend our Kubernetes/Run:AI environment for GPU scheduling, quota management, and multi tenant workload isolation across research and engineering teams company wide.
• Own fleet health: monitoring, alerting, and rapid triage of hardware and network faults (bad transceivers, GPU Xid errors, NCCL/collective failures, RoCE congestion).
• Onboard engineers and researchers onto the platform and act as their escalation point, working directly alongside them to debug, train, and optimize their workloads whenever infrastructure, not the model, is the bottleneck.
• Partner with product facing teams across Anduril to understand emerging compute needs and translate them into platform capability.

Skills

GPU compute at scale

Automation

Observability

Troubleshooting

Leadership

Tools

Kubernetes

Run:AI

Ray

NCCL

NVLink

InfiniBand

Job description

Anduril Industries is seeking a Senior AI Infrastructure Engineer to own the stability and expansion of GPU training infrastructure. You will lead deployment, tuning, and automation across Kubernetes, Run:AI, Ray, and high-speed interconnects to support ML research and production workloads.

This hands-on role requires deep experience with GPU systems (H200/H300), large-scale cluster design, and sustaining uptime while enabling fast, reliable model training and inference.