Principal Systems Software Engineer, Semiconductor Systems Inspection at NVIDIA

Department: Engineer, Sys SW

Compensation

$272,000 – $431,250/yr

Setup: On-site
Location: California
Type: Full-time
Level: staff+
Posted: Sep 16, 2026

Description

NVIDIA is hiring a Principal Systems Software Engineer for Semiconductor Inspection in Santa Clara to architect and deliver production-ready AI systems for optical and e-beam inspection, wafer and mask inspection, metrology, defect review, equipment signals, and process data. The role covers foundation models, computer vision, time-series and multimodal AI, anomaly detection, model adaptation, evaluation, agentic workflows, inference optimization, and deployment in air-gapped fab environments. Candidates need an MS or PhD in a relevant technical field or equivalent experience, 12+ years of software or algorithm engineering, 8+ years in semiconductor manufacturing, and strong deep-learning and Python experience. The base salary is 272,000 USD to 431,250 USD, with equity and benefits.

Required Skills

Computer vision
Time-series modeling
Multimodal AI
Anomaly detection
Model adaptation
Production inference
Python
PyTorch
GPU literacy

Additional Information

Minimum Education: Master's or PhD in Computer Science, Electrical Engineering, Computer Engineering, or a related technical field, or equivalent experience
Required Previous Experiences: 12+ years of professional software or algorithm engineering with technical ownership and architectural direction
8+ years in semiconductor manufacturing, including inspection, metrology, defect review, process control, yield engineering, or fab-data systems
Hands-on delivery of software shipped in semiconductor equipment, operated in a fab, or 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
Preferred Qualifications: 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