Senior HPC Systems Engineer - GPU AI Infra

Anduril Industries, Inc.; Costa Mesa (CA); On-site; USD 147,000 - 196,000; Full time

Benefits offered by this job

Equity grants

Comprehensive benefits

Job summary

Anduril Industries, Inc. is seeking an HPC System Engineer to design, deploy, and maintain large-scale compute infrastructure. You’ll work with cutting-edge NVIDIA hardware and software to support sensitive national security programs.

Expect collaboration with engineers across the globe, rigorous security practices, and leadership responsibilities. You will assemble and optimize GPU clusters, configure job schedulers, and ensure scalable storage while mentoring teammates and guiding

Qualifications

• Experience with parallel/distributed file systems (Lustre, GPFS, etc.).
• Proficient in HPC batch scheduling software (SLURM, PBSPro).
• Ability to lift 50 lbs; eligible for US Top Secret clearance.

Responsibilities

• Architect and deploy GPU infrastructure including H100/H200 and B200/B300 systems.
• Rack, cable, and configure physical servers and multi-node GPUs end-to-end.
• Configure HPC environments, including Slurm, multi-user login, and cluster management.
• Implement high-speed interconnects (NVLink, InfiniBand) for large-scale training.
• Manage large-scale storage platforms; optimize AI/ML data access patterns.
• Install and maintain software stack (CUDA, PyTorch, TensorFlow) on HPC clusters.
• Provide guidance and architectural leadership to the team.

Skills

HPC concepts

Cluster architecture

System automation

SLURM

Ansible

Tools

Slurm batch system

NVIDIA GPUs

Warewulf

NVIDIA Base Command

DCGM

Job description

Anduril Industries, Inc. is seeking an HPC System Engineer to design, deploy, and maintain large-scale compute infrastructure. You’ll work with cutting-edge NVIDIA hardware and software to support sensitive national security programs.

Expect collaboration with engineers across the globe, rigorous security practices, and leadership responsibilities. You will assemble and optimize GPU clusters, configure job schedulers, and ensure scalable storage while mentoring teammates and guiding