Systems Software Engineer - AI Infrastructure

About the role

To make electronics, you need chips, wafers, transistors, reticles, and... To make these, you must see, test and manufacture them at scale—faster and better than ever before. That's where KLA comes in. Whether you're early in your career or an experienced professional, you'll solve complex challenges, work alongside brilliant minds and help shape the future of technology.

Group/Division

The Semiconductor Products and Customers (Semi PC) business unit designs, builds and sells KLA’s product portfolio to help chip manufacturers meet high-quality standards for technologies like AI, data centers, automotive and electronic devices.

Our inspection, metrology, and analytics systems detect and monitor defects across logic and memory chips, specialty semiconductors, wafers, reticles, advanced packaging, process equipment and materials. We also develop technologies for specialty processes, IC substrates, and PCB manufacturing, including etch and deposition, component inspection, and imaging and analytics.

At KLA, physics and data are at the core of everything we do. Our AI and Modeling Center of Excellence drives innovation across KLA products by combining expertise in physics modeling, high-performance computing (HPC) and AI. The group is comprised of top-tier physicists, machine learning engineers, HPC architects and data scientists that collaborate to develop scalable, cutting-edge solutions for our customers.

We’re hiring across multiple disciplines—this is your opportunity to make a global impact.

What You'll Do

In this role, you will play a key part in advancing business priorities by delivering high-impact work across your area of expertise.

KLA is seeking a Senior AI Infrastructure Engineer to design, build, and support scalable infrastructure for advanced AI and machine learning workloads. This role will focus on high-performance computing (HPC), Kubernetes, GPU-accelerated infrastructure, and distributed computing environments supporting model training and inference.

• The ideal candidate will have a strong background in HPC, or large-scale compute infrastructure combined with hands-on Kubernetes experience. Experience supporting AI/ML platforms, MLOps, LLMOps, or GPU-based workloads is highly desirable.
• This position will work closely with AI/ML engineers, software engineers, infrastructure teams, and other technical stakeholders to build reliable, scalable, and high-performance computing environments.

Responsibilities

• Design, deploy, and maintain scalable HPC and GPU computing infrastructure supporting AI and machine learning workloads.
• Build and operate production Kubernetes and containerized computing environments.
• Manage and optimize distributed compute environments, including workload scheduling using Slurm or similar technologies.
• Support infrastructure for AI/ML model training, distributed training, model serving, and inference.
• Optimize compute, GPU, networking, and storage performance for demanding AI and engineering workloads.
• Develop automation for infrastructure provisioning, deployment, monitoring, and operations using Python, Bash, Go, or similar technologies.
• Implement Infrastructure as Code and CI/CD practices to improve scalability and reliability.
• Monitor system performance and troubleshoot complex issues across Linux, Kubernetes, GPU, networking, storage, and distributed computing environments.
• Partner with AI/ML teams to support modern MLOps and LLMOps workflows.
• Evaluate and implement emerging technologies related to AI infrastructure, GPU orchestration, distributed computing, and model serving.
• Implement security, access control, monitoring, and operational best practices across AI and HPC infrastructure.
• Collaborate with cross-functional engineering and infrastructure teams to define scalable technical architectures and standards.

Minimum Qualifications

• Degree in Computer Science, Computer Engineering, Electrical Engineering, or a related technical field, or equivalent practical experience.
• Strong experience with HPC, GPU infrastructure, or large-scale distributed computing environments.
• Hands-on experience deploying and supporting Kubernetes in production environments.
• Strong Linux systems administration and infrastructure engineering experience.
• Experience with containers and technologies such as Docker.
• Experience with HPC workload schedulers such as Slurm or similar technologies.
• Experience with scripting or automation using Python, Bash, Go, or similar languages.
• Understanding of distributed systems, networking, storage, system performance, and infrastructure reliability.
• Strong troubleshooting and problem-solving skills in complex production environments.

Preferred Qualifications

• Experience supporting AI/ML infrastructure, MLOps, or LLMOps environments.
• Experience with GPU-accelerated computing, distributed model training, or model serving/inference.
• Experience with AI/ML technologies such as Ray, PyTorch, or TensorFlow.
• Experience with high-performance networking or storage in large-scale compute environments.
• Experience with Infrastructure as Code and automation using tools such as Terraform or Helm.

Minimum Qualifications

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

Base Pay Range

$136,300.00 - $231,700.00 Annually

Primary Location: USA-CA-Milpitas-KLA

KLA’s total rewards package for employees may also include participation in performance incentive programs and eligibility for additional benefits including but not limited to: medical, dental, vision, life, and other voluntary benefits, 401(K) including company matching, employee stock purchase program (ESPP), student debt assistance, tuition reimbursement program, development and career growth opportunities and programs, financial planning benefits, wellness benefits including an employee assistance program (EAP), paid time off and paid company holidays, and family care and bonding leave.

Interns are eligible for some of the benefits listed. Our pay ranges are determined by role, level, and location. The range displayed reflects the pay for this position in the primary location identified in this posting. Actual pay depends on several factors, including state minimum pay wage rates, location, job-related skills, experience, and relevant education level or training.

We are committed to complying with all applicable federal and state minimum wage requirements where applicable. If applicable, your recruiter can share more about the specific pay range for your preferred location during the hiring process.

Use of AI Statement

At KLA, our interviews seek to understand your individual skills, problem-solving approach and authentic thinking. Use of these tools may interfere with our ability to evaluate your individual qualifications and affect your candidacy. KLA is committed to advancing innovation through responsible AI, and we value candidates who share this mindset.