Senior Applied ML Engineer – Physics-Driven Systems & Optimization

Overview

About Keysight AI Labs

Keysight’s AI Labs is a global 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.

 

 

About the AI Team 

Join Keysight's central AI Hub in the heart of Barcelona. We are expanding our newly formed AI Team. As part of this growing team, you will join a vibrant, cross-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.

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

 

 

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