Machine-learning engineers need reliable access to GPUs to train and evaluate models. Shield AI’s AI Factory provides shared compute and development tools for these workflows, both for our engineers and for customer deployments.
We are looking for a Platform Engineer to make that compute platform easier to use, more reliable and more efficient. You will write software, improve how teams share GPUs, help developers troubleshoot workloads and use performance data to guide capacity growth. This is a hands-on software and platform engineering role.
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.
Developers can get the GPU resources they need through understandable access and resource policies.
Teams can diagnose slow or blocked training jobs and distinguish capacity shortages from configuration problems.
GPU expansion decisions reflect measured demand and workload performance.
Qualification
Practical LinuxExperience with Run
Required
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.
Preferred
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.
