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Semiconductor Modeling Jobs (NOW HIRING)

cam

Semiconductor Mechanical Installer

Georgetown, TX · On-site

Semiconductor Mechanical Installer Overview: CAM Industrial Solutions, a leader in industrial ... Read, interpret, and follow blueprints, shop and field drawings, 2D/3D models (isometrics) for ...

Micross

Package Design Engineer | Semiconductor

Apopka, FL · On-site

$119K/yr

Bachelors degree in Electrical Engineering, Mechanical Engineering, or other semiconductor packaging related discipline. * 8 to 10 years of experience in semiconductor packaging design, modeling, and ...

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Semiconductor Modeling information

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$59K

$89.8K

$122.5K

How much do semiconductor modeling jobs pay per year?

As of Jun 25, 2026, the average yearly pay for semiconductor modeling in the United States is $89,784.00, according to ZipRecruiter salary data. Most workers in this role earn between $62,000.00 and $111,000.00 per year, depending on experience, location, and employer.

What is the highest salary in the semiconductor industry?

Senior semiconductor modeling engineers and specialists with extensive experience and advanced skills can earn salaries exceeding $150,000 to $200,000 annually, especially in high-cost regions or at leading companies. Top executives and those with specialized expertise in areas like device physics or EDA tools may earn even higher compensation, including bonuses and stock options.

Is modeling a good paying job?

Semiconductor modeling is generally a well-paying profession, especially for those with strong skills in physics, electrical engineering, and simulation tools. Salaries can vary based on experience, location, and certifications, but it is considered a lucrative field within the technology industry.

How to become a semiconductor chip designer?

To become a semiconductor chip designer, typically a bachelor's degree in electrical engineering, computer engineering, or a related field is required. Gaining experience with electronic design automation (EDA) tools, understanding semiconductor physics, and developing skills in digital and analog circuit design are essential. Advanced roles may require a master's or Ph.D. and knowledge of industry standards and fabrication processes.

What is semiconductor device modelling?

Semiconductor device modelling involves creating mathematical representations of semiconductor components, such as transistors and diodes, to predict their electrical behavior under various conditions. This process is essential for designing, simulating, and optimizing integrated circuits and often requires knowledge of physics, circuit theory, and specialized software tools like SPICE.

What is the difference between Semiconductor Modeling vs Semiconductor Design Engineer?

AspectSemiconductor ModelingSemiconductor Design Engineer
Required CredentialsBachelor's or Master's in Electrical Engineering, VLSI, or related fields; knowledge of simulation toolsBachelor's or Master's in Electrical Engineering, VLSI, or related fields; proficiency in CAD tools
Work EnvironmentResearch labs, simulation environments, design verification teamsDesign teams, fabrication facilities, R&D departments
Industry UsageUsed for device behavior prediction, circuit simulation, and performance analysisInvolved in creating actual chip layouts, circuit design, and prototype development

Semiconductor Modeling focuses on simulating and predicting device and circuit behavior using software tools, while Semiconductor Design Engineers develop the actual chip layouts and circuits. Both roles require similar educational backgrounds and often collaborate during product development, but their core responsibilities differ significantly.

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Semiconductor Modeling jobs near you

Senior Systems Software Engineer, Semiconductor Systems Inspection

NVIDIA AI

Santa Clara, CA • On-site

Full-time

Posted 8 days ago

Job description

Job Summary:
NVIDIA AI has been a leader in computer graphics and accelerated computing for over 25 years. They are seeking a Senior Systems Software Engineer to develop AI products focused on semiconductor inspection, including system architectures and workflows for defect analysis.
Responsibilities:
• Define and prototype AI system architectures for semiconductor defect inspection across optical inspection, 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.
• Work with our partners to integrate and enhance existing computer vision and multimodal inspection workflows for defect detection, classification, localization, segmentation, nuisance filtering, ADC, and ADR.
• Design agentic inspection flows for air-gapped fab environments, connecting data triage, model inference, review assistance, root-cause analysis, human approval, and secure deployment constraints.
• Use 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.
• Work with our partners to address noisy, limited, and shifting fab data, including tool-to-tool calibration, domain-shift mitigation, synthetic defect generation, noise simulation, and augmentation.
• Convert research into customer-ready semiconductor inspection products with clear evaluation, failure analysis, monitoring, optimization, and production deployment paths.
• Partner with research, software, process, metrology, inspection, review, and hardware teams to define roadmap priorities for next-generation semiconductor AI inspection systems.
Qualifications:
Required:
• MS, or PhD in Computer Science, Electrical Engineering, Computer Engineering, or a related technical field, or equivalent experience.
• 3+ years of proven experience in deep learning, machine learning, computer vision, or applied AI.
• Strong programming skills in Python 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.
• Familiarity with self-supervised, few-shot, weakly supervised, unsupervised, or domain adaptation approaches relevant to inspection problems.
• Strong analytical, communication, and cross-functional collaboration skills.
Preferred:
• 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, training, evaluation, export, and inference in production settings.
Company:
Explore the latest breakthroughs made possible with AI. Founded in , the company is headquartered in Santa Clara, CA, US, , with a team of 10001+ employees. The company is currently Late Stage.

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