Senior AI Infra Engineer - GPU Cluster Architect for Scale

Telemetry Today LLC; Costa Mesa (CA); On-site; USD 166,000 - 220,000; Full time

Job summary

Anduril Industries is seeking a Senior AI Infrastructure Engineer to own the vision and stability of GPU-based training at scale. You will build self-healing, automated deployment tooling, optimize NCCL and high-speed networking, and ensure Kubernetes, Run:AI, and Ray scheduling run faultlessly across research and engineering teams.

This role requires 10+ years in HPC/infrastructure, hands-on experience with H200/B200/B300 GPUs, and the ability to obtain a U.S. Top Secret clearance.

Qualifications

• 10+ years in hands-on infrastructure, HPC, or datacenter engineering for GPU compute.
• Experience with H200/B200/B300 or similar GPU systems including firmware/driver management.
• Experience with high-performance interconnects (NVLink, InfiniBand, RoCE, Spectrum-X) in large GPU clusters.
• Kubernetes required; Run:ai or similar GPU scheduling experience is highly desirable.
• Strong automation background; build repeatable deployment pipelines.

Responsibilities

• Rack, stack, cable, and bring up GPU compute hardware (H200/B200/B300).
• Build and tune the interconnect fabric (NVLink, InfiniBand, RoCE, Spectrum-X).
• Integrate high-performance parallel storage (VAST, DDN, Weka) for large datasets.
• Automate cluster deployment end-to-end with infrastructure as code.
• Operate and extend Kubernetes/Run:AI for GPU scheduling and multi-tenant workloads.
• Own fleet health: monitoring, alerting, and rapid triage of hardware faults.
• Onboard researchers and translate compute needs into platform capabilities.
• Partner with product teams to align compute with roadmaps.

Job description

Anduril Industries is seeking a Senior AI Infrastructure Engineer to own the vision and stability of GPU-based training at scale. You will build self-healing, automated deployment tooling, optimize NCCL and high-speed networking, and ensure Kubernetes, Run:AI, and Ray scheduling run faultlessly across research and engineering teams.

This role requires 10+ years in HPC/infrastructure, hands-on experience with H200/B200/B300 GPUs, and the ability to obtain a U.S. Top Secret clearance.