Platform Engineer AI Compute (6034)

Shield AI•San Diego, CA

7d•$167,600 - $251,500

About The Position

Shield AI is seeking a hands-on Platform Engineer to improve the usability, reliability, and efficiency of its AI Factory compute platform. The platform provides shared GPU compute and development tools for machine-learning engineers, both for internal workflows and customer deployments. The role involves building software and platform capabilities, improving how teams share GPUs, helping developers troubleshoot workloads, and using performance data to guide capacity growth. The engineer will build and maintain Kubernetes-based GPU workload infrastructure; design resource quotas, scheduling priorities, permissions, and isolation; and automate deployments and upgrades of GPU services. The role also investigates workload delays, failures, and performance issues, builds diagnostics relating platform metrics to developer experience, benchmarks workloads to inform hardware and capacity decisions, and delivers repeatable installations across cloud, on-premises, and air-gapped environments. The role contributes to the reference architecture used by Shield AI engineering teams and delivered to customers developing AI capabilities. Its work supports research iteration, efficient compute utilization, and the transition of AI systems from experimentation to operational deployment.

Requirements

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 translate recurring problems into maintained platform capabilities.

Nice To Haves

Experience with Run:ai or comparable GPU scheduling and resource-management systems.
Familiarity with distributed training and tools such as Ray or PyTorch.
Experience delivering on-premises or air-gapped platforms, or contributing to open-source infrastructure.

Responsibilities

Build and maintain 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, using infrastructure as code and tools such as Terraform and Helm.
Investigate why jobs wait, fail, or run slowly, and address recurring scheduling, configuration, storage, and networking problems.
Build diagnostics connecting queue times, GPU utilization, memory requirements, and training throughput to the developer experience.
Benchmark representative workloads and recommend when to add GPU capacity, how much capacity to add, and which hardware fits the workload.
Deliver repeatable installations and upgrades across cloud, on-premises, and air-gapped environments.
Work directly with machine-learning engineers to understand workloads and turn recurring problems into maintained platform capabilities.