Principal Systems Software Engineer, Semiconductor Systems Inspection

NVIDIA has transformed computer graphics, PC gaming, and accelerated computing for more than 25 years through exceptional technology and the people who build it. In semiconductor manufacturing, our role is to enable the ecosystem, not compete within it. We partner with fabs, equipment manufacturers, and software providers to make inspection, metrology, and manufacturing intelligence dramatically faster on the NVIDIA platform.

Our team builds the software that makes this possible: models, adaptation and evaluation workflows, and deployable inference capabilities that partners integrate into their own tools. We work in environments where labeled data is limited and proprietary, distributions shift across tools and fabs, production budgets are tight, and software must operate inside air-gapped facilities.

We’re seeking a Principal Systems Software Engineer for Semiconductor Inspection in Santa Clara. This is a hands-on architect role: you will define the approach, build it, evaluate it, and demonstrate the results. You will work across computer vision, time-series modeling, multimodal AI, anomaly detection, model adaptation, evaluation, and production inference. Success means technology that a fab or equipment vendor can integrate, operate, and trust—not only a successful internal demonstration. 

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  Define and prototype AI system architectures spanning optical and e-beam inspection, wafer and mask inspection, metrology, defect review, equipment signals, and process data.

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  Advance world foundation model capabilities for semiconductor manufacturing, including vision, time-series and multimodal representation learning, model adaptation, domain transfer, and data-scarce defect understanding.

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  Develop workflows for defect detection, classification, localization, segmentation, nuisance filtering, ADC, ADR, and equipment time-series anomaly detection.

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  Establish evaluation methods that measure model quality and robustness under tool-to-tool and fab-to-fab domain shift, diagnose failures, and define production-readiness thresholds.

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  Design fab-local evaluation and adaptation workflows that preserve customer data sovereignty and produce comparable evidence across partner deployments.

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  Connect inspection and metrology results with equipment signals, process history, wafer genealogy, SPC, and yield outcomes to support manufacturing-wide reasoning and root-cause analysis.

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  Design agentic workflows for air-gapped fabs that connect data triage, model inference, review assistance, root-cause analysis, human approval, and secure deployment.

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  Optimize and package inference capabilities to meet real production latency, memory, throughput, reliability, and security budgets, allowing partners to deploy without NVIDIA operating their systems.

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  Work directly with fabs and equipment manufacturers to move capabilities from prototype to partner-operated production.

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  Collaborate across research, software, process, metrology, inspection, and hardware teams to define platform capabilities and the technical roadmap for semiconductor AI.

• MS or PhD in Computer Science, Electrical Engineering, Computer Engineering, or a related technical field, or equivalent experience.

• 12+ years of professional software or algorithm engineering, including demonstrated technical ownership and architectural direction. Directly relevant PhD research may count toward this total; research alone cannot satisfy the domain and production-delivery requirements below.

• 8+ years in semiconductor manufacturing, including inspection, metrology, defect review, process control, yield engineering, or fab-data systems.

• Hands-on delivery of software that shipped in semiconductor equipment, operated in a fab, or was adopted by a manufacturing customer.

• 4+ years of hands-on deep learning, machine learning, computer vision, or applied AI, including work within the last 24 months.

• Strong Python skills and production experience with PyTorch or TensorFlow.

• A track record of taking ambiguous technical problems through architecture, implementation, evaluation, and validated results.

• Working GPU literacy, including the ability to reason about inference performance and evaluate hardware and software bottlenecks.

• Direct engagement with fabs, manufacturing teams, or equipment vendors on technical requirements.

• Strong analytical, communication, and cross-functional leadership skills.

• Experience adapting large pretrained vision, multimodal, or world foundation models to specialized industrial domains.

• Background in anomaly detection and generation, time-series modeling, FDC, virtual metrology, or advanced-packaging inspection.

• Experience designing evaluation frameworks for sparse proprietary data and shifting production distributions.

• Expertise in synthetic data, self-supervised or few-shot learning, domain adaptation, model compression, and inference optimization using NVIDIA technologies.

• Experience building SDKs, platform capabilities, or agentic workflows adopted by external engineering teams.

With competitive salaries and a generous benefits package, NVIDIA is widely considered one of the technology industry’s most desirable employers. Our engineering teams are growing in some of the most consequential areas of manufacturing and semiconductor technology. If you are a hands-on Principal Systems Software Engineer who combines semiconductor expertise with architectural judgment and a drive to put advanced AI into production, we want to hear from you.

This posting is for an existing vacancy. 

NVIDIA uses AI tools in its recruiting processes.