AI Engineer for Technology Innovation
Be at the forefront of technological advancements and contribute to delivering breakthroughs in electronic design, simulation, prototyping, test, manufacturing, and optimization. Our team creates world-class solutions in various markets, serving customers across 100 countries.
Our company culture values a bold vision of where technology can take us and a passion for tackling challenging problems with innovative solutions. We believe that when people feel a sense of belonging, they can be more creative, thrive at all points in their careers, and make an immediate impact.
We are expanding our AI team and you'll have the chance to shape our AI strategy. Our work spans supervised and unsupervised learning, generative models, multimodal systems, reinforcement learning, and large language models.
As part of this growing team, you'll collaborate closely with hardware engineers, domain scientists, and product software developers to bring AI models from research into production tools used globally. You will :
- Design, implement, and deploy state-of-the-art ML architectures that merge physics insights, numerical optimization, and modern AI techniques.
- Contribute to building scalable and explainable ML systems, from geometry-aware GNNs and Transformers to reinforcement learning and generative models.
- Partner with experts in RF, EM, circuit, and measurement domains to translate physical constraints and design workflows into ML-ready formulations.
- Develop advanced optimization and control methods : Bayesian, gradient-based, and gradient-free optimization.
- Apply reinforcement learning (PPO, DDPG, SAC) for continuous tuning and control tasks.
Required Qualifications :
Master's or PhD in Applied Mathematics, Scientific Computing, Computer Science, Electrical Engineering, or related field.5+ years of experience applying scientific computing and optimization to real-world problems.Strong hands-on experience with modern ML architectures (GNNs, Transformers, Vision Models, Neural Operators).Practical experience with generative models (GANs, VAEs, Diffusion).Background in Bayesian and numerical optimization and hyperparameter tuning.Applied experience with reinforcement learning (PPO, DDPG, SAC).Proficiency in Python, C++, CUDA, and GPU performance optimization.Experience with multi-GPU / distributed training in HPC or cloud (Slurm, MPI, AWS).Desired Qualifications :
Experience applying ML / RL / generative models to parameter tuning, data augmentation, or design exploration.Familiarity with Keysight simulation tools (ADS, RFPro, EMPro, Signal Studio, RaySim).Publications or patents in scientific ML, generative modeling, RL, or optimization.This is an Equal Opportunity Employer.