Platform Engineer: AI Compute & GPU Orchestration

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

Job summary

Shield AI is seeking a Platform Engineer to make the GPU compute platform easier to use, more reliable, and more efficient. You will write software, automate GPU service deployments, and help developers troubleshoot workloads across cloud, on-prem, and air‑gapped environments.

In this hands‑on role, you’ll design resource sharing across teams, measure performance, and guide capacity planning while collaborating with machine-learning engineers to turn recurring problems into durable platform

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

Python

Go

Networking

Tools

Kubernetes

Terraform

Helm

Run:ai

PyTorch

Ray

Job description

Shield AI is seeking a Platform Engineer to make the GPU compute platform easier to use, more reliable, and more efficient. You will write software, automate GPU service deployments, and help developers troubleshoot workloads across cloud, on-prem, and air‑gapped environments.

In this hands‑on role, you’ll design resource sharing across teams, measure performance, and guide capacity planning while collaborating with machine-learning engineers to turn recurring problems into durable platform