Principal Systems Software Engineer, Semiconductor Systems Inspection

NVIDIA•Santa Clara, CA

1d•$272,000 - $431,250•Onsite

About The Position

NVIDIA is expanding its semiconductor inspection capabilities, focusing on turning a strong technical foundation into practical AI products for semiconductor manufacturing. The team is developing anomaly-generation and inspection workflows for semiconductor manufacturing, addressing challenges like limited data, domain shifts, and stringent fab deployment requirements. This role involves developing the next generation of AI products for semiconductor analysis, making promising approaches production-ready for key manufacturing projects. The work spans computer vision, multimodal AI, anomaly detection, model compression, and deployment optimization within a small, high-impact core team focused on moving research into deployable products.

Requirements

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.

Nice To Haves

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.

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 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.