HPC Systems Engineer

KLA•Milpitas, CA

4d•$136,300 - $231,700•Onsite

About The Position

As an AI Infrastructure Engineer, you will research, evaluate, and develop next-generation hardware and software technologies that enable large-scale AI and machine learning workloads across KLA. You will help design and build the infrastructure that powers model training, inference, networking, storage, data protection, and emerging agentic AI systems. This role sits at the intersection of systems engineering, AI platform development, MLOps, and DevOps, ensuring that AI teams have reliable, scalable, high-performance infrastructure to develop, train, deploy, and operate AI solutions. You will also define and develop reference architectures and platform standards that can be embraced across KLA products and engineering organizations, enabling secure, cost-effective, and repeatable AI infrastructure deployments.

Requirements

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 cluster
Doctorate (Academic) Degree and 0 years related work experience; Master's Level Degree and related work experience of 3 years; Bachelor's Level Degree and related work experience of 5 years
Linux administration
Kubernetes/Docker
Slurm
Ray
TensorFlow
PyTorch
GPU/TPU optimization
containerization
orchestration
programming skills
High speed networking
storage systems
virtualization
server management
Understanding of model training, fine‑tuning, RAG pipelines, and agentic AI systems
Bash
Python
CI/CD pipelines
infrastructure as code (IaC)
TPM-based encryption
Kubernetes security (RBAC, OPA/Gatekeeper)
container security

Responsibilities

Design & Build AI Infrastructure: Architect, deploy, and operate scalable, secure, and cost-effective AI platforms, distributed training environments, and model serving systems.
Research & Innovation: Evaluate emerging technologies in AI infrastructure, compute, storage, networking, orchestration, and security, and develop reference designs for enterprise adoption.
Hardware & Software Orchestration: Bring up, integrate, and lead AI compute infrastructure while diagnosing hardware, firmware, operating system, and platform issues.
Data & Storage Management: Design and maintain high-performance storage, networking, and data pipelines supporting large-scale AI workloads.
Performance & Reliability: Develop observability and monitoring solutions, optimize AI workload performance, and ensure infrastructure meets reliability and availability targets.
Security & Compliance: Implement security-by-design principles including encryption, identity and access management, secrets management, and AI data security. Contribute to the architecture and implementation capabilities for protecting AI workloads, intellectual property, and sensitive data at KLA.
Collaboration: Partner with AI researchers, data scientists, software engineers, IT, and security teams to align platform capabilities with business and product needs.