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.

Senior AI Infrastructure Engineer, Physical Infrastructure

Responsibilities

Qualification

Required

Preferred

Benefits

Company

Anduril Industries

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