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
Key Responsibilities
- Design, train, and deploy computer vision models for object detection, classification, and positioning in semiconductor service environments
- Build robust image and video processing pipelines that handle real-world field conditions
- Develop and maintain training data pipelines: collection, annotation, augmentation, and quality assurance
- Optimize models for inference performance across deployment targets - balancing accuracy, latency, and compute constraints
- Integrate vision capabilities into broader multimodal AI systems that combine visual perception with knowledge retrieval and reasoning
- Build evaluation frameworks that measure model performance against real field data
- Design model architectures that serve both AI knowledge systems and future robotics/automation initiatives
- Stay current with the vision model frontier and bring what's relevant into production
- Document model architectures, training procedures, and deployment patterns so the team can build on your work
Qualifications
- Bachelor's degree in Computer Science, Electrical Engineering, or related field Master's preferred
- 1+ years of experience building and deploying computer vision systems (strong new grads with demonstrated projects or research considered)
- Strong proficiency in Python and deep learning frameworks (PyTorch, TensorFlow)
- Hands-on experience with object detection and classification architectures and transfer learning / fine-tuning approaches
- Experience building training data pipelines - annotation tooling, data augmentation, and active learning strategies
- Experience with model optimization techniques for edge or constrained deployment
- Strong understanding of image processing fundamentals and camera systems
- Self-directed - you experiment, iterate, and push boundaries without waiting for direction
- Excellent problem-solving skills and ability to debug model failures in messy, real-world data
- Prior experience in manufacturing, robotics, industrial inspection, or semiconductor environments is a plus
- Experience with 3D vision, depth estimation, or point cloud processing is a plus
Minimum Qualifications
Master's Level Degree and 0 years related work experience Bachelor's Level Degree and related work experience of 2 years
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