Join to apply for the Sr Applied ML Engineer – Physics-Driven Systems & Optimization role at Keysight Technologies Overview Keysight is on the forefront of technology innovation, delivering breakthroughs and trusted insights in electronic design, simulation, prototyping, test, manufacturing, and optimization.
class solutions in communications, 5G, automotive, energy, quantum, aerospace, defense, and semiconductor markets for customers in over 100 countries.
About Keysight AI Labs Keysight's AI Labs is a general R&D; group pioneering the integration of machine learning, generative AI into Keysight's test, measurement, and design solutions.
Our mission is to transform how engineers design, simulate, and validate advanced systems—from 6G and semiconductors to quantum and automotive—by embedding AI throughout our workflows.
functional environment that brings together experts in ML engineering, data science, physics?informed modeling, and software development.
You'll work closely with domain experts across RF, EM, circuit design, and test & measurement to accelerate scientific innovation through AI.

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.
Graph Neural Networks (GNNs) for geometry/topology?aware modeling Transformers for sequential and multimodal data Vision Models (CNNs, ViTs) for field? based detection Generative Models (GANs, Diffusion) for data augmentation and design candidate generation Apply advanced optimization and control methods: 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.
ready code in Python, C++, and CUDA, integrating with CI/CD pipelines and performance profiling tools.
Collaborate with product teams to embed AI/ML?based optimization and generative modules into Keysight software.
Qualifications 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?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) Excellent communication and collaboration skills across cross?functional teams 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 Experience deploying ML/RL systems in production or embedded workflows Keysight is an Equal Opportunity Employer.
Not Applicable Employment type: Full?time Job function: Engineering and Information Technology Industries: Appliances, Electrical, and Electronics Manufacturing #J-*-Ljbffr

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