> Markdown version of [/jobs/ext/3709081-systems-software-engineer-ai-infrastructure](https://www.wearedevelopers.com/jobs/ext/3709081-systems-software-engineer-ai-infrastructure). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Systems Software Engineer - AI Infrastructure

- **Company:** KLA Corporation
- **Location:** Milpitas, CA, United States
- **Experience:** Experienced
- **Salary:** $136,300.0 - $199,900.0
- **Contract:** Permanent contract
- **Skills:** Kubernetes Security, Artificial Intelligence, Systems Engineering, Bash Shell, Computer Programming, Computer Engineering, Continuous Integration, Data Security, DevOps, Distributed Computing Environment, Firmware, Identity and Access Management, Python (Programming Language), Key Management, Linux System Administration, Machine Learning, Role-Based Access Control, Tensorflow, Server Administration, Software Engineering, Virtualization Technology, AI Infrastructure, Graphics Processing Unit (GPU), Data Storage Management, Pytorch, Retrieval-Augmented Generation, Transfer Learning, Infrastructure as Code (IaC), Agentic-AI, Build Management, Containerization, AI Platforms, Kubernetes, Information Technology, Slurm, Machine Learning Operations, Data Pipelines, Docker
- **Published:** October 13, 2026
- **Apply:** https://jobs.localjobnetwork.com/apply/add/87730824/1

## About the Role

* Technical Skills: Linux administration, Kubernetes/Docker, Slurm, Ray, TensorFlow, PyTorch, GPU/TPU optimization, containerization, orchestration, and programming skills.
 * Infrastructure Knowledge: High speed networking, storage systems, virtualization, and server management.
 * AI/ML Integration: Understanding of model training, finetuning, RAG pipelines, and agentic AI systems
 * Automation & Scripting: Bash, Python, CI/CD pipelines, infrastructure as code (IaC).
 * Security: TPM-based encryption, Kubernetes security (RBAC, OPA/Gatekeeper), container security., * Degree in Computer Science, Computer Engineering, or related field
 * 5-8 years in systems engineering, DevOps, or ML infrastructure
 * Handson 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

## Description

As an Systems Software Engineer - AI Infrastructure, 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.
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.