Senior Platform Engineer, AI Compute & GPU Kubernetes

Frontdoor Defense; San Diego (CA); On-site; USD 200,000 - 300,000; Full time

Benefits offered by this job

Bonus

Equity

Benefits

Job summary

Shield AI is seeking a Senior Staff Platform Engineer to build and operate a GPU-enabled Kubernetes platform for ML workloads. You will design shared GPU access, automate deployments, and optimize performance across cloud, on‑premises, and air‑gapped environments. You’ll partner with ML engineers to scale training and improve reliability.

Your work enables rapid experimentation and deployment of autonomous systems, leveraging Run:ai‑like scheduling and open‑source tools in a defense context.

Qualifications

• Experience building production Kubernetes platforms with GPU workloads.
• Proficient in Python or Go for platform tooling.
• Experience automating infrastructure deployment and upgrades.
• Solid Linux, networking and storage troubleshooting skills.
• Familiar with shared resource scheduling and access controls.
• Ability to translate ML workloads into reliable platform capabilities.

Responsibilities

• Build and maintain software and configurations for GPU workloads on Kubernetes.
• Design quotas, priorities, permissions, and isolation for multi-team use.
• Automate deployment and upgrades of GPU services using IaC tools.
• Diagnose job delays, failures and performance issues and fix root causes.
• Create diagnostics linking queue times, GPU usage and throughput to developers.
• Benchmark workloads and advise on capacity planning and hardware fit.
• Deliver repeatable installations across cloud, on-premises, and air-gapped environments.

Skills

Kubernetes platform experience

Python

Go

Infrastructure automation

Linux troubleshooting

Networking troubleshooting

Workload isolation / access controls

Job description

Shield AI is seeking a Senior Staff Platform Engineer to build and operate a GPU-enabled Kubernetes platform for ML workloads. You will design shared GPU access, automate deployments, and optimize performance across cloud, on‑premises, and air‑gapped environments. You’ll partner with ML engineers to scale training and improve reliability.

Your work enables rapid experimentation and deployment of autonomous systems, leveraging Run:ai‑like scheduling and open‑source tools in a defense context.