AI Infrastructure Systems Engineer

KLA; Milpitas, Northern (CA, KY); Hybrid; USD 136,000 - 200,000; Full time

Benefits offered by this job

Medical benefits

401(k) with company match

Employee stock purchase program (ESPP)

Tuition reimbursement

Job summary

KLA in Milpitas, CA is seeking a Systems Software Engineer - AI Infrastructure to research, design, and build scalable AI platforms and model serving systems. You will help enable training, inference, storage, networking, and security for enterprise AI workloads.

You will collaborate with AI researchers, data scientists, and software engineers to define reference architectures, ensure reliability, and apply security best practices across the stack.

Qualifications

• Degree in Computer Science, Computer Engineering, or related field.
• 5–8 years in systems engineering, DevOps, or ML infrastructure.
• Hands-on experience building AI/GPU clusters.

Responsibilities

• Architect, deploy, and operate scalable, secure AI platforms and model serving systems.
• Evaluate emerging AI infra technologies and design reference designs for enterprise adoption.
• Bring up, integrate, and lead AI compute infrastructure while diagnosing hardware/software issues.
• Design and maintain high-performance storage and data pipelines for large-scale AI workloads.
• Develop observability, monitoring, and reliability enhancements for AI workloads.

Skills

Linux administration

Kubernetes/Docker

Slurm

Ray

TensorFlow

PyTorch

GPU/TPU optimization

Containerization

Orchestration

Python

CI/CD

IaC

Education

Bachelor's Degree in Computer Science or Computer Engineering

Master's Degree in a related field

Doctorate (Academic) Degree

Tools

Kubernetes

Docker

Slurm

Ray

Job description

KLA in Milpitas, CA is seeking a Systems Software Engineer - AI Infrastructure to research, design, and build scalable AI platforms and model serving systems. You will help enable training, inference, storage, networking, and security for enterprise AI workloads.

You will collaborate with AI researchers, data scientists, and software engineers to define reference architectures, ensure reliability, and apply security best practices across the stack.