Senior Staff ML Platform Engineer (Kubernetes AI Factory)

F7d8749a; San Diego (CA); On-site; USD 180,000 - 260,000; Full time

Job summary

Shield AI is seeking a Senior Staff Engineer to define and build the AI Factory Reference Architecture, a Kubernetes-native platform for developing, training, evaluating, and deploying next-generation AI models. You will partner with ML researchers and platform engineers to deliver a scalable, secure, and reusable tech stack across cloud and air-gapped environments.

This hands-on role spans architecture and implementation, contributing to tooling, workflows, and governance that accelerate

Qualifications

• Experience building Kubernetes-native AI or MLOps platforms for distributed ML workloads.
• Deep understanding of PyTorch, Hugging Face Transformers, and distributed training techniques.
• Experience operating GPU-equipped infrastructure and distributed training systems.
• Proficiency with Kubernetes, Linux, networking, security, storage, and distributed systems.
• Experience with GPU scheduling concepts and large-scale AI workloads.
• Experience packaging and deploying cloud-native infrastructure using Terraform and Helm.
• Strong Python and Golang software engineering skills and modern cloud-native technologies.
• Ability to translate research workflows into scalable platform capabilities.

Responsibilities

• AI Platform Development: Lead the design and implementation of the AI Factory Reference Architecture with a Kubernetes-native platform.
• AI Research Enablement: Collaborate with ML researchers to support training workflows and state-of-the-art frameworks.
• Developer Experience: Create self-service AI development workflows from local experiments to distributed execution.
• Distributed AI Infrastructure: Build infrastructure for distributed training, simulation, inference, and RL workloads.

Tools

Terraform

Helm

Ray

Job description

Shield AI is seeking a Senior Staff Engineer to define and build the AI Factory Reference Architecture, a Kubernetes-native platform for developing, training, evaluating, and deploying next-generation AI models. You will partner with ML researchers and platform engineers to deliver a scalable, secure, and reusable tech stack across cloud and air-gapped environments.

This hands-on role spans architecture and implementation, contributing to tooling, workflows, and governance that accelerate