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.
Stay current with the latest ML, RL, and generative AI research; evaluate and prototype promising new techniques.
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-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)
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
Careers Privacy Statement***Keysight is an Equal Opportunity Employer.***
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.
Stay current with the latest ML, RL, and generative AI research; evaluate and prototype promising new techniques.
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 (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)
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
Careers Privacy Statement***Keysight is an Equal Opportunity Employer.***
