Platform Engineer, AI Compute & GPU Orchestration

Shield AI; San Mateo (CA); On-site; USD 200,000 - 300,000; Full time

Benefits offered by this job

Bonus

Benefits

Equity

Job summary

Shield AI is seeking a Senior Staff Engineer to build and scale the AI Factory compute platform for GPU workloads. You will enable multi‑team access on Kubernetes, improve reliability, and guide capacity growth across cloud, on‑premises, and air‑gapped environments.

The role is hands‑on software and platform engineering, partnering with machine‑learning engineers to translate workload patterns into robust platform capabilities that accelerate research and deployment of autonomous systems.

Qualifications

• Experience building and supporting production Kubernetes platforms, including GPU or distributed computing workloads.
• Strong software engineering skills in Python, Go or a comparable language.
• Experience automating infrastructure deployment, configuration and upgrades.
• Practical Linux, networking and storage troubleshooting skills.
• Experience with shared resource scheduling, access controls or workload isolation.
• Ability to work directly with machine-learning engineers to address recurring problems.

Responsibilities

• Build and maintain GPU workload tooling on Kubernetes.
• Design quotas, scheduling priorities, permissions and isolation for multi‑team sharing.
• Automate deployment and upgrades of GPU services using IaC (Terraform, Helm).
• Investigate job delays and optimize scheduling, configuration, storage, and networking.
• Develop diagnostics linking queue times, GPU utilization, memory, and training throughput.
• Benchmark workloads and advise on GPU capacity and hardware fit.
• Deliver repeatable installations across cloud, on-premises, and air-gapped environments.

Skills

Kubernetes platforms

Python

Go

Infrastructure automation

Linux troubleshooting

Networking troubleshooting

Workload isolation

ML workloads

Tools

Run:ai

Job description

Shield AI is seeking a Senior Staff Engineer to build and scale the AI Factory compute platform for GPU workloads. You will enable multi‑team access on Kubernetes, improve reliability, and guide capacity growth across cloud, on‑premises, and air‑gapped environments.

The role is hands‑on software and platform engineering, partnering with machine‑learning engineers to translate workload patterns into robust platform capabilities that accelerate research and deployment of autonomous systems.