Descripción del Puesto
Keysight
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
about what we do.
Our
award-winning
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
Keysight's central AI team in Barcelona
, 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.
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.**
