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 Deep Learning Engineer , you will design, build,



and deploy advanced deep learning models and scalable ML systems that power real‑world applications.

You will contribute across the full ML lifecycle — from research and experimentation to large-scale training and production deployment — ensuring high standards in performance, reliability, and maintainability.

This role requires strong hands‑on experience with modern deep learning architectures and distributed training techniques, as well as the ability to translate research concepts into efficient, production-ready implementations.

Responsibilities

Design and implement advanced deep learning architectures, including:

Transformer‑based models

Sequence and representation learning models

Prediction, classification, and representation learning tasks

Generative modeling approaches (diffusion models, autoregressive models, VAEs)

Build and optimize scalable training pipelines:

Distributed training using DDP, FSDP, DeepSpeed or similar frameworks

Multi‑GPU and multi‑node training environments

Improve model efficiency and performance:

Memory optimization techniques

GPU utilization profiling and optimization

Develop production‑ready ML systems

Model serving and inference optimization

Deployment pipelines and integration into production environments

Monitoring, validation, and performance tracking





Contribute to engineering excellence

Write high‑quality, maintainable, and well‑tested code

Apply best practices in modular design, testing, and CI/CD

Participate in design reviews and architecture discussions

Stay current with advances in deep learning and evaluate new methods for practical applicability.

Required Qualifications

Master’s or PhD in Computer Science, Machine Learning, Artificial Intelligence, or a related technical field

5+ years of hands‑on experience developing deep learning or machine learning systems in production or research environments

Strong experience with modern deep learning frameworks

Strong understanding of deep learning fundamentals:

Optimization techniques and training dynamics

Experience training models at scale:

Multi‑GPU or multi‑node distributed environments

Experience with C++ and/or CUDA is a plus

Experience optimizing model performance and training efficiency

Solid software engineering practices

Testing and CI/CD pipeline

Modular and maintainable code design

Strong collaboration and communication skills in cross‑functional environments

Desired Qualifications

Experience training or deploying large‑scale foundation models or generative models

Experience with model optimization techniques:

Distillation

Experience working with distributed compute environments or HPC clusters

Contributions to open‑source ML projects, publications, or technical communities

Experience working in interdisciplinary environments involving scientific or engineering data

Careers Privacy Statement: Keysight is an Equal Opportunity Employer.

📌 Senior Deep Learning Engineer (PyTorch, Distributed Training) (Madrid)
🏢 Keysight Technologies
📍 Madrid