Senior Platform Engineer — AI Factory (Kubernetes)

Fdetimes; San Mateo (CA); On-site; USD 180,000 - 240,000; Full time

Job summary

Shield AI seeks a Senior Staff Engineer to define and build the AI Factory Reference Architecture, a Kubernetes-native platform for AI development, training, evaluation, and deployment. You will partner with ML researchers, platform engineers, and autonomy teams to deliver a seamless developer experience across cloud, on-prem, and air-gapped environments.

You will balance researcher productivity, platform simplicity, and scalability while shaping architecture and implementation across the stack

Qualifications

• Experience building Kubernetes-native AI or MLOps platforms for distributed ML workloads.
• Deep knowledge of PyTorch, Hugging Face Transformers, and distributed training.
• Experience operating GPU-accelerated infrastructure and distributed training systems.

Responsibilities

• Lead design and implementation of the Kubernetes-native AI Factory Reference Architecture for AI development, training, evaluation, and deployment.
• Collaborate with ML researchers to support evolving training workflows and state-of-the-art frameworks.
• Design self-service AI development workflows enabling seamless local experimentation to large-scale distributed execution.
• Build infrastructure for distributed training, simulation, inference, and RL workloads with scalable orchestration and resource management.
• Design shared GPU infrastructure across cloud and on-prem environments to improve utilization and reliability.
• Enable dataset management, experiment tracking, artifact management, and model versioning.
• Develop repeatable deployment and lifecycle management using Infrastructure as Code.

Skills

Kubernetes AI Platform

Distributed ML training

AI Platform design

Developer Experience

Tools

PyTorch

Hugging Face Transformers

GPU infrastructure management

Job description

Shield AI seeks a Senior Staff Engineer to define and build the AI Factory Reference Architecture, a Kubernetes-native platform for AI development, training, evaluation, and deployment. You will partner with ML researchers, platform engineers, and autonomy teams to deliver a seamless developer experience across cloud, on-prem, and air-gapped environments.

You will balance researcher productivity, platform simplicity, and scalability while shaping architecture and implementation across the stack