Platform Engineer - AI Compute & GPU Kubernetes

Shield; San Diego (CA); On-site; USD 140,000 - 190,000; Full time

Benefits offered by this job

Equity

Bonus

Benefits

Job summary

Shield AI in San Diego, CA is seeking a Platform Engineer to make our AI Factory compute platform easier to use, more reliable, and more efficient. You will build and maintain GPU workloads on Kubernetes and automate deployments of GPU services such as Run:ai.

You'll design quotas, priorities, and isolation for multi-team access, troubleshoot workloads, and translate performance data into capacity decisions that scale with the business.

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, understand their workloads and turn recurring problems into maintained platform capabilities.

Responsibilities

• Build and maintain the software and configuration that let developers run GPU workloads on Kubernetes.
• Design quotas, scheduling priorities, permissions and isolation so multiple teams can share compute predictably.
• Automate deployment and upgrades of GPU services, including Run:ai, with infrastructure as code and tools such as Terraform and Helm.
• Investigate why jobs wait, fail or run slowly, and fix recurring scheduling, configuration, storage and networking problems.
• Build diagnostics that connect queue times, GPU utilization, memory requirements and training throughput to the developer’s experience.
• Benchmark representative workloads and recommend when to add GPU capacity, how much to add and which hardware fits the workload.
• Deliver repeatable installations and upgrades across cloud, on-premises and air-gapped environments.

Skills

Kubernetes platforms

Python/Go

Automation/infrastructure

Linux networking storage

Shared resource scheduling

Tools

Run:ai

Ray

PyTorch

Job description

Shield AI in San Diego, CA is seeking a Platform Engineer to make our AI Factory compute platform easier to use, more reliable, and more efficient. You will build and maintain GPU workloads on Kubernetes and automate deployments of GPU services such as Run:ai.

You'll design quotas, priorities, and isolation for multi-team access, troubleshoot workloads, and translate performance data into capacity decisions that scale with the business.