Senior AI Infra Engineer: GPU Training & Automation

Anduril-1; Seattle (WA); On-site; USD 166,000 - 220,000; Full time

Job summary

Anduril Industries in Seattle, WA is seeking a Senior AI Infrastructure Engineer to own the stability and scale of our GPU training cluster. You’ll automate deployment, tune NCCL, optimize Kubernetes, Run:AI, and Ray, and build self-healing systems to keep massive ML workloads running with minimal manual intervention.

You will lead GPU hardware bring‑up, interconnect tuning (NVLink/InfiniBand), and network storage integration while collaborating with research and product teams to translate

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

HPC

Kubernetes

NCCL tuning

Automation

Observability

Run:AI

Ray scheduling

NVLink/InfiniBand

Tools

H200/B200/B300 GPUs

NVIDIA NVL72

NVLink

InfiniBand

RoCE

Spectrum-X

VAST

DDN

Weka

Lustre

DCGM

Job description

Anduril Industries in Seattle, WA is seeking a Senior AI Infrastructure Engineer to own the stability and scale of our GPU training cluster. You’ll automate deployment, tune NCCL, optimize Kubernetes, Run:AI, and Ray, and build self-healing systems to keep massive ML workloads running with minimal manual intervention.

You will lead GPU hardware bring‑up, interconnect tuning (NVLink/InfiniBand), and network storage integration while collaborating with research and product teams to translate