Senior Deep Learning Engineer (PyTorch, Distributed Training)

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

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Our 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 , 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

• Vision architectures (CNNs, Vision Transformers)
• Sequence and representation learning models

• Develop models for:

• 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

• Mixed precision training
• 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. xohynlm

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:

• PyTorch (preferred) or TensorFlow

• Strong understanding of deep learning fundamentals:

• Neural network architectures (Transformers, CNNs, sequence models)

• Optimization techniques and training dynamics

• Experience training models at scale:

• Multi-GPU or multi-node distributed environments

• Strong programming skills:

• Python

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

• Experience optimizing model performance and training efficiency
• Solid software engineering practices:

• Version control (Git)

• Testing and CI/CD pipelin
• 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:

• Quantization

• Pruning
• Distillation

• Experience designing reusable ML infrastructure or internal ML frameworks
• 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

*Keysight is an Equal Opportunity Employer.