This role pays more than
99%
of similar roles. Most pay
$169,550–$215,175
— the shaded band above.
At the midpoint, this role pays about
$352k
versus about
$192k
for comparable roles.
Based on 240 similar postings.
Employer
About Nvidia
Nvidia is a leading designer of graphics processing units (GPUs) and system-on-chip units, powering gaming, professional visualization, data centers, and artificial intelligence workloads. Industry: Semiconductors & AI Computing
Nvidia currently has
896 open roles
on FindRole.
Listed pay typically runs
$184,000–$287,500
across 876 roles with salary data.
Principal Systems Software Engineer, Semiconductor Systems Inspection will join a high-impact core team to develop the next generation of AI products for semiconductor analysis. This role involves defining and prototyping AI system architectures for defect inspection across optical and e-beam systems, wafer and mask inspection, metrology, and defect-review workflows. You will build production-ready tools for anomaly detection, model compression, and deployment optimization while addressing challenges like limited data and domain shifts in manufacturing environments. Key responsibilities include advancing multimodal representation learning, designing agentic inspection flows for air-gapped fab environments, and integrating computer vision for classification and segmentation. Required skills include Python, PyTorch or TensorFlow, and experience with deep learning frameworks. You will utilize specialized knowledge of semiconductor metrology, including CD, LER, and wafer maps, to improve model quality and provide robust decision support in industrial inspection scenarios.
Define and prototype AI system architectures for semiconductor defect inspection across optical and e-beam workflows.
Advance multimodal representation learning and model adaptation to address data-scarce defect understanding.
Integrate computer vision workflows for defect detection, classification, localization, segmentation, and nuisance filtering.
Design agentic inspection flows for air-gapped fab environments including triage, inference, and root-cause analysis.
Apply semiconductor metrology and process context to improve model quality and manufacturing decision support.
Mitigate domain shifts and noise through tool-to-tool calibration, synthetic defect generation, and data augmentation.
Convert research into production-ready products with clear evaluation, failure analysis, and deployment optimization.
Collaborate across hardware, software, and process teams to set priorities for next-generation inspection systems.
What we're looking for
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.
Experience with semiconductor inspection, industrial visual inspection, manufacturing AI, metrology, or defect-review workflows (preferred).
Experience with model compression, quantization, pruning, or familiarity with NVIDIA software and deployment tools like TensorRT and CUDA (preferred).
Systems Software
Firmware
Hardware Provisioning
API
Monitoring
Observability
Concurrency
Threat Modeling
Debugging
Troubleshooting
Data Centers
Storage Systems
Networking
GPU Subsystems
C
C++
Python
Linux Kernel
Embedded Linux
RTOS
Device Drivers
GPU
Middleware
System Architecture
Performance Engineering
Virtualization
ISO 26262
AUTOSAR
ASPICE
AI/ML