At Cadence, we hire and develop leaders and innovators who want to make an impact on the world of technology.
Cadence Design Systems is a world leader in providing computational software for all aspects of intelligent system design.
You will be part of a cross-disciplinary R&D team working on the emerging boundary of scientific computing and machine learning / AI.
The candidate should have an advanced degree in computer science / electrical engineering and the following preferred skills :
Working knowledge of contemporary methods in deep learning / AI (LLMs, graph neural networks, transformer architectures, CNNs, reinforcement learning, representation learning, transfer learning, etc.)
Demonstrated programming proficiency in Python and C++
Experience and demonstrated proficiency with machine learning frameworks such as PyTorch and TensorFlow
Demonstrated ability to reduce algorithms and theoretical knowledge to practice
Experience in physical-level scientific computation (computational electromagnetics, thermal analysis, electrical circuit simulation, photonic systems) is a plus
Knowledge of statistical inference techniques (significance testing, Monte Carlo methods, random sampling, density estimation, design of experiments, Bayesian statistics) is a plus
Familiarity with recent research trends in physics-informed machine learning, e.g. physics-informed neural networks, neural operator theory, DeepONets is a plus
Past experience with optimization algorithms is a plus
Candidate should expect to work with a cross-functional engineering team to perform cutting-edge research but ultimately deliver innovative technologies in a production environment.
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