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Semiconductor Modeling Jobs (NOW HIRING)

Nvidia

Senior Package Modeling Engineer

Santa Clara, CA · Hybrid

$122K - $168K/yr

Join us in an outstanding opportunity as a Senior Package Modeling Engineer in the Advanced ... Practical understanding of advanced semiconductor packaging architecture and materials (flip-chip ...

cam

Semiconductor Sheet Metal Mechanic

Round Rock, TX

$22.50 - $28/hr

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

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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
Lead AI Engineer -- Semiconductor AI Innovation

Lead AI Engineer -- Semiconductor AI Innovation

Onto Innovation

Wilmington, MA • On-site

$112K - $147K/yr

Full-time

Posted 5 days ago

Job description

Job Summary:
Onto Innovation is a leader in semiconductor technologies, and they are seeking a Lead AI Engineer to drive AI-powered solutions for semiconductor equipment operations. This hands-on leadership role involves defining AI strategies, mentoring a team, and integrating AI solutions into production environments.
Responsibilities:
• Define the AI strategy and architecture for integrating machine learning into core engineering and manufacturing processes.
• Partner with tool, process, and applications engineers to map as-is processes and define a to-be AI/automation architecture and deliver measurable outcomes.
• Ship agentic assistants for use-cases. Stand up LLM + RAG + tool integrations (via MCP servers) that help engineers accelerate tool operation/setup/maintenance and explain trade-offs, grounded in internal docs, logs, and historical inspection outcomes.
• Lead projects across diverse areas: Predictive maintenance for tool health monitoring and failure detection. Computer vision for wafer defect detection, segmentation, and classification. LLM-based engineering assistants using RAG and MCP agents to make internal knowledge more accessible. Process optimization & yield improvement through data-driven insights and parameter tuning. Simulation and digital twins to model process behaviors and accelerate R&D.
• Build retrieval-augmented AI assistants to query internal knowledge bases, tools, and logs.
• Architect robust pipelines for data ingestion, labeling, storage, and retrieval across massive multi-modal datasets (images, telemetry, recipes, logs).
• Stand up scalable MLOps infrastructure: model registries, monitoring, evaluation, deployment, and governance.
• Hire, mentor, and manage a team of 3 engineers focused on LLM/Agents, CV/ML, and MLOps/Data.
• Work cross-functionally to integrate AI solutions into production environments safely and securely.
Qualifications:
Required:
• 5+ years applied ML/AI experience, with 3+ years in a technical leadership role.
• Hands-on expertise with at least two of the following domains: Large Language Models - RAG, fine-tuning, agent frameworks, prompt optimization; Predictive Modeling - tool failure prediction, anomaly detection, time-series analysis; Computer Vision - defect detection, segmentation, or SEM/optical imaging.
• Strong background in ML systems architecture and production deployment.
• Advanced Python proficiency: C++/CUDA familiarity is a plus.
• Experience with MLOps stacks: containers, CI/CD, Ray Serve/Triton, model registries (e.g., MLflow), and GPU optimization.
• Strong stakeholder collaboration skills and the ability to translate between engineering, operations, and leadership.
• Demonstrated success delivering AI-powered products into production.
Preferred:
• Familiarity with semiconductor manufacturing, inspection, or metrology.
• Understanding of fab interfaces and data connectivity (SECS/GEM, GEM300).
• Prior experience deploying digital twins or simulation-driven optimization.
• Knowledge of vector databases, retrieval pipelines, and hybrid search.
• Experience implementing safety, security, and IP protections for AI systems.
• Exposure to datasets or tools from KLA, ASML, Applied Materials, Onto, Nova, or similar inspection/metrology vendors.
Company:
Onto Innovation stands alone in process control with our unique perspective across the semiconductor value chain. Founded in 2019, the company is headquartered in Wilmington, USA, with a team of 1001-5000 employees. The company is currently Late Stage.

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