Principal AI Systems Engineer - Semiconductor Inspection

NVIDIA Corporation

Santa Clara (CA)

On-site

USD 272,000 - 431,000

Full time

14 days+

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Job summary

NVIDIA Corporation in Santa Clara is seeking a Principal Software Engineer for Systems Inspection to advance AI products for semiconductor analysis. You will translate research into production-ready workflows spanning computer vision, multimodal AI, anomaly detection, and deployment optimization.

Lead architecture, collaborate across teams, and ensure robust, scalable solutions for defect inspection, metrology, and review workflows.

Qualifications

MS or PhD in Computer Science, Electrical Engineering, Computer Engineering, or a related technical field.
15+ years of proven experience in systems design, architecture, and software development.
4+ years of current experience in deep learning, machine learning, computer vision, or applied AI.
Strong Python skills and experience with modern deep learning frameworks such as PyTorch or TensorFlow.
Experience developing or applying foundational world models in computer vision for classification, detection, segmentation, anomaly detection, or multimodal understanding.
A record of technical leadership in domain-adaptation approaches relevant to inspection problems.
Strong analytical, communication, and cross-functional collaboration skills.

Responsibilities

Define and prototype AI system architectures for semiconductor defect inspection across optical and e-beam inspection, wafer and mask inspection, metrology, and defect-review workflows.
Advance multimodal representation learning, domain transfer, and data-scarce defect understanding in inspection systems.
Partner with customers and internal teams to integrate and improve computer vision and multimodal workflows for defect detection, classification, localization, segmentation, nuisance filtering, ADC, and ADR.
Design agentic inspection flows for air-gapped fab environments that connect data triage, model inference, review assistance, root-cause analysis, human approval, and secure deployment constraints.
Apply semiconductor metrology, inspection, review, and process context—including CD, LER, LWR, overlay, wafer maps, defect maps, SPC signals, and yield signals—to improve model quality and fab decision support.
Turn research into customer-ready semiconductor inspection products with clear approaches to evaluation, failure analysis, monitoring, optimization, and production deployment.
Work with research, software, process, metrology, inspection, review, and hardware teams to set priorities for next-generation semiconductor AI inspection systems.