Job description

NVIDIA has transformed computer graphics, PC gaming, and accelerated computing for more than 25 years through exceptional technology and the people who build it. We’re expanding our semiconductor inspection capabilities in a strategically important area, turning a strong technical foundation with customers and partners into practical AI products: models, adaptation workflows, and inference pipelines built for real inspection environments and tight deployment budgets. Our team is developing anomaly-generation and inspection workflows for semiconductor manufacturing, where limited data, domain shifts, and stringent fab deployment requirements are everyday constraints. We’re seeking a Principal Software Engineer for Systems Inspection in Santa Clara to develop the next generation of AI products for semiconductor analysis. You’ll take promising approaches and make them production-ready for key manufacturing projects, working across computer vision, multimodal AI, anomaly detection, model compression, and deployment optimization. You’ll join a small, high-impact core team focused on moving research into deployable products while raising model quality, robustness, and operational readiness for demanding industrial inspection scenarios. What you’ll be doing: • 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 WFM capabilities for semiconductor inspection, including multimodal representation learning, model adaptation, domain transfer, and data-scarce defect understanding. • 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. • Address noisy, limited, and shifting fab data through tool-to-tool calibration, domain-shift mitigation, synthetic defect generation, noise simulation, and augmentation. • 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. What we need to see: • MS or PhD in Computer Science, Electrical Engineering, Computer Engineering, or a related technical field, or equivalent experience. • 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. Ways to stand out from the crowd: • Experience with semiconductor inspection, industrial visual inspection, manufacturing AI, metrology, or defect-review workflows. • Experience with knowledge distillation, model compression, quantization, pruning, or deployment optimization for edge or production environments. • Background in anomaly detection or anomaly generation, especially in domains with unusual labels and shifting visual distributions. • Familiarity with NVIDIA software and deployment tools such as TensorRT, CUDA, cuDNN, Triton, DeepStream, TAO Toolkit, or RAPIDS. • Experience building end-to-end pipelines that span data curation and training. With competitive salaries and a generous benefits package, NVIDIA is widely considered to be one of the technology industry's most desirable employers. We have some of the most forward-thinking and versatile people in the world working with us, and our engineering teams are growing fast in some of the most impactful fields of our generation: Manufacturing and Semiconductor. If you're a creative Principal Systems Software Engineer who enjoys autonomy and shares our passion for technology, we want to hear from you. Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 272,000 USD - 431,250 USD. You will also be eligible for equity and benefits . Applications for this job will be accepted at least until September 18, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes. NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

Requirements

Must-haves

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 WFM capabilities for semiconductor inspection, including multimodal representation learning, model adaptation, domain transfer, and data-scarce defect understanding.
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.
Address noisy, limited, and shifting fab data through tool-to-tool calibration, domain-shift mitigation, synthetic defect generation, noise simulation, and augmentation.
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.
MS or PhD in Computer Science, Electrical Engineering, Computer Engineering, or a related technical field, or equivalent experience.
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.
Experience with semiconductor inspection, industrial visual inspection, manufacturing AI, metrology, or defect-review workflows.
Experience with knowledge distillation, model compression, quantization, pruning, or deployment optimization for edge or production environments.
Background in anomaly detection or anomaly generation, especially in domains with unusual labels and shifting visual distributions.
Familiarity with NVIDIA software and deployment tools such as TensorRT, CUDA, cuDNN, Triton, DeepStream, TAO Toolkit, or RAPIDS.
Experience building end-to-end pipelines that span data curation and training.
You will also be eligible for equity and benefits .
Applications for this job will be accepted at least until September 18, 2026.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.

Nice-to-haves

None flagged.

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Keywords to mirror

aimachine learningpython

Platform: eightfold
Effort: medium

Benefits

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