Device Modeling AI/ML Software Engineer

Keysight is at the forefront of technology innovation, delivering breakthroughs and trusted insights in electronic design, simulation, prototyping, test, manufacturing, and optimization. Our ~16,800 employees create world-class solutions in communications, 5G, automotive, energy, quantum, aerospace, defense, and semiconductor markets for customers in over 100 countries. Learn more about what we do. Our award-winning culture embraces a bold vision of where technology can take us and a passion for tackling challenging problems with industry-first solutions. We believe that when people feel a sense of belonging, they can be more creative, innovative, and thrive at all points in their careers. At Keysight EDA, we are transforming semiconductor device modeling and characterization through the application of artificial intelligence, machine learning, and advanced software engineering. Our industry-leading products, including IC-CAP, WaferPro Express, Model Builder Program (MBP), and Model Quality Assurance (MQA) , enable foundries, IDMs, and semiconductor companies worldwide to extract, validate, and deploy compact transistor models for advanced process technologies. As a Device Modeling AI/ML Software Engineer , you will be responsible for developing intelligent software solutions that accelerate semiconductor characterization, compact model extraction, model validation, and device modeling workflows. You will work closely with software engineers, AI/ML experts, compact modeling specialists, and semiconductor domain experts to integrate next-generation AI technologies into Keysight's device modeling solutions. Responsibilities • Design and develop AI/ML-driven solutions for semiconductor device characterization and compact model extraction workflows • Develop software features for IC-CAP, WaferPro, MBP, MQA, and related device modeling products • Apply machine learning, optimization, and statistical modeling techniques to improve model extraction accuracy and productivity • Build scalable data processing, training, validation, and deployment pipelines for AI-powered characterization workflows • Develop AI-assisted solutions for parameter extraction, model calibration, quality assessment, and anomaly detection • Research and implement surrogate modeling techniques to reduce simulation and optimization turnaround time • Evaluate and integrate generative AI, agentic AI, and LLM-based workflows to improve engineering productivity • Collaborate with device modeling experts to develop solutions for advanced CMOS, RF, analog, power, and emerging semiconductor technologies • Contribute to software architecture, performance optimization, testing, code reviews, and product releases • Work closely with global R&D teams to define future AI-driven semiconductor modeling solutions Qualifications • Master's, or PhD degree in Electrical Engineering, Computer Engineering, Computer Science, Applied Physics, Semiconductor Engineering, EDA/CAD, Data Science, or a related technical field • 5+ years of experience in software development for scientific, engineering, EDA, or semiconductor applications. • Strong programming skills in Python and object-oriented software development • Experience with machine learning frameworks such as PyTorch, TensorFlow, or scikit-learn • Solid understanding of data structures, algorithms, and software engineering principles • Strong analytical, debugging, and problem-solving skills • Experience developing software applications in a collaborative engineering environment • Interest in applying AI/ML technologies to semiconductor device modeling and electronic design automation Preferred Qualifications • Experience with semiconductor devices, compact modeling, semiconductor physics, RF devices, analog/mixed-signal design, or EDA technologies • Familiarity with device characterization, parameter extraction, compact model development, SPICE modeling, or semiconductor measurement workflows • Experience with surrogate modeling, Bayesian optimization, parameter estimation, machine learning regression, anomaly detection, or predictive modeling techniques • Familiarity with IC-CAP, WaferPro Express, MBP, MQA, TCAD, SPICE, or related semiconductor modeling tools • Experience processing and analyzing large-scale characterization or measurement datasets • Knowledge of AI-assisted engineering workflows, generative AI, agentic AI systems, or LLM-based applications • Experience with C++, Rust, or performance-oriented software development • Experience developing software on Windows and Linux platforms • Familiarity with Git, CI/CD pipelines, and modern software development practices • Experience with cloud computing, distributed computing, or scalable ML infrastructure • Passion for applying AI and machine learning to accelerate semiconductor design and modeling workflows