KLA is a global leader in diversified electronics for the semiconductor manufacturing ecosystem. Virtually every electronic device in the world is produced using our technologies. No laptop, smartphone, wearable device, voice-controlled gadget, flexible screen, VR device or smart car would have made it into your hands without us. KLA invents systems and solutions for the manufacturing of wafers and reticles, integrated circuits, packaging, printed circuit boards and flat panel displays. The innovative ideas and devices that are advancing humanity all begin with inspiration, research and development. KLA focuses more than average on innovation and we invest 15% of sales back into R&D. Our expert teams of physicists, engineers, data scientists and problem-solvers work together with the world's leading technology providers to accelerate the delivery of tomorrow's electronic devices. Life here is exciting and our teams thrive on tackling really hard problems. There is never a dull moment with us.
The KLA Services team headquartered in Milpitas, CA is our service organization that consists of Service Sales and Marketing, Spares Supply Chain management, Field Operations, Engineering, Product Training, and Technical Support. The KLA Services organization partners with our field teams and customers in all business sectors to maintain the high performance and productivity of our products through a flexible portfolio of services. Our comprehensive services include: proactive management of tools to identify and improve performance expertise in optics, image processing and motion control with worldwide service engineers, 24/7 technical support teams and knowledge management systems and an extensive parts network to ensure worldwide availability of parts.
Job Description/Preferred Qualifications
KLA is seeking a Product Engineer - Computer Science, Data Analytics & Edge AI to design, build, and scale data‑driven and edge‑intelligent solutions that improve service productivity, tool availability, and decision‑making across KLA's global installed base.
This role focuses on transforming raw tool, sensor, and service data into real‑time insights using a combination of edge analytics, AI‑enabled diagnostics, and scalable data pipelines. The engineer will help determine what analytics belong in the cloud, at the edge, or directly on tools and robots, enabling faster response, reduced latency, and greater robustness in fab environments.
Key Responsibilities
Data Analytics & Insights
- Develop analytics and diagnostics using large‑scale tool, sensor, and service data, including logs, time‑series, and event‑based data.
- Create health indicators, anomaly detection, and KPIs that directly improve MTTR, uptime, and service efficiency.
- Analyze fleet‑level trends to identify systemic reliability and performance issues.
Edge AI & Smart Diagnostics
- Design and implement edge analytics and Edge AI solutions that run close to the source of data (on tools, robots, sensors, or local compute).
- Enable real‑time anomaly detection, early warning, and predictive diagnostics without dependence on continuous cloud connectivity.
- Support integration of Edge AI models into service platforms, collaborative robots, and smart sensor architectures.
Software & Data Engineering
- Build and maintain scalable data pipelines supporting both edge and cloud analytics workflows.
- Develop backend services and libraries to support edge inference events, metadata, and feedback loops.
- Ensure data quality, traceability, and robustness across distributed, hybrid (edge + cloud) systems.
AI & Advanced Analytics Enablement
- Prepare datasets for AI/ML workflows, including labeling strategies, feature extraction, and validation pipelines.
- Collaborate with AI teams on model deployment, tuning, and lifecycle management at the edge and in the cloud.
- Support analytics that integrate with robotics, machine vision, and automation systems.
Minimum Qualifications
- Bachelor's degree (or higher) in Computer Science, Data Science, Software Engineering, or related field.
- Strong foundation in software development and data analytics.
- Experience working with large datasets, log analytics, or time‑series data.
- Proficiency in Python (or similar) for data processing and analysis.
- Understanding of distributed systems and data flows.
- Experience with Edge AI, embedded analytics, or smart sensor systems.
- Exposure to AI/ML model deployment, particularly in constrained or real‑time environments.
Preferred Qualifications
- Familiarity with industrial, semiconductor, or service analytics environments.
- Experience designing systems that balance latency, compute, bandwidth, and accuracy.
- Ability to move fluidly between exploratory analysis and production‑grade engineering.
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