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Senior Applied ML Engineer - Physics-Driven Systems & Optimization

hace 3 días

Barcelona

is on the forefront of technology innovation, delivering breakthroughs and trusted insights in electronic design, simulation, prototyping, test, manufacturing, and optimization. Our ~15,000 employees create world-class solutions in communications, 5G, automotive, energy, quantum, aerospace, defense, and semiconductor markets for customers in over 100 countries. Learn more ¿Listo para inscribirse? Antes de hacerlo, asegúrese de leer todos los detalles pertenecientes a este trabajo en la descripción a continuación. Our 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. About Keysight AI Labs Join , a newly formed hub driving innovation in machine learning. As part of this growing team, you’ll have the chance to shape our AI strategy and make an immediate impact. Our work spans supervised and unsupervised learning, generative models, multimodal systems, reinforcement learning, and large language models. About the AI Team We are expanding the Team and You’ll join a cross-disciplinary AI & Modeling team in the heart of Barcelona. The Team develops physics-informed, data-driven, and reinforcement learning systems that accelerate design, measurement, and optimization processes across domains such as RF, EM, circuits, and advanced instrumentation. The group collaborates closely with hardware engineers, domain scientists, and product software developers to bring AI models from research into production tools used globally. About the Role As a Senior Applied Machine Learning Engineer , you will design, implement, and deploy state-of-the-art ML architectures that merge physics insights, numerical optimization, and modern AI techniques. You’ll contribute to building scalable and explainable ML systems, from geometry-aware GNNs and Transformers to reinforcement learning and generative models, that drive design automation, anomaly detection, and optimization in Keysight’s next-generation platforms. Responsibilities • Partner with Keysight experts in RF, EM, circuit, and measurement domains to translate physical constraints and design workflows into ML-ready formulations., • Design and implement advanced ML architectures:, • Graph Neural Networks (GNNs) for geometry/topology-aware modeling, • Transformers for sequential and multimodal data, • Vision Models (CNNs, ViTs) for field- or spectrogram-based detection, • Generative Models (GANs, Diffusion) for data augmentation and design candidate generation, • Apply advanced optimization and control methods:, • Bayesian, gradient-based, and gradient-free optimization, • Reinforcement Learning (PPO, DDPG, SAC) for continuous tuning and control tasks, • Develop scalable training and inference pipelines (multi-GPU, HPC, AWS) ensuring efficiency and reliability., • Write production-ready code in Python, C++, and CUDA , integrating with CI/CD pipelines and performance profiling tools., • Benchmark ML and RL models against physics simulators and measurement datasets for robustness and reproducibility., • Collaborate with product teams to embed AI/ML-based optimization and generative modules into Keysight software., • 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 (e.g., RF, EM, or measurement systems), • 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), • Solid software-engineering discipline (testing, CI/CD, modular design), • 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

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