Senior ML Engineer – Scientific & Engineering Data
Keysight Technologies · Barcelona, Spain · R&D
Keysight is at 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 the Team
At Keysight, we build advanced software and AI systems that power engineering innovation across electronics, communications, automotive, energy, aerospace, and semiconductors.
This role sits within Keysight’s Applied AI & Autonomy initiative, a multidisciplinary R&D effort developing intelligent, agent-based systems that learn from real-world engineering data, simulations, and measurements. The team combines machine learning, data engineering, and scientific modeling to create adaptive, explainable AI for complex engineering workflows.
About the Role
As a Senior Machine Learning Engineer, you will design and develop machine-learning models and data systems that learn from engineering data and continuously improve through feedback from simulations and measurements.
This is a hands-on, applied ML role, focused on:
Scientific and engineering datasets
Model generalization and robustness
Explainability and trust in predictions
You will work closely with simulation engineers, measurement experts, and software developers to bring ML into real engineering decision-making.
Responsibilities
- Design and train ML models that capture engineering and physics-driven behaviour
- Build and maintain data pipelines for structured, semi-structured, and experimental data
- Develop feedback loops where new data triggers model updates or retraining
- Implement explainable AI (XAI) techniques to make model decisions transparent and traceable
- Create diagnostics for model performance, drift, uncertainty, and anomalies
- Collaborate with domain experts to align ML models with real-world engineering use cases
- Continuously validate and refine models using real measurement and simulation data
Qualifications
Required Qualifications
MSc or PhD or 5+ years of hands-on experience in machine learning, data science, or scientific computing
Strong foundations in applied machine learning (model training, evaluation, generalization)
Experience working with complex, real-world datasets (engineering, scientific, or industrial)
Proficiency in Python and ML frameworks such as PyTorch
Solid experience in data preprocessing, feature engineering, and pipeline automation
Experience building interpretable or explainable ML models
Comfortable collaborating in cross-functional, international teams
Desired Qualifications
Experience with scientific computing, simulation-driven ML, or surrogate models
Knowledge of physics-informed or hybrid ML approaches
Familiarity with uncertainty estimation, sensitivity analysis, or confidence scoring
Exposure to MLOps tools (e.g. MLflow, DVC) and experiment tracking
Experience with high-performance or GPU-based training environments
Some C++ exposure for performance-critical components
Careers Privacy Statement***Keysight is an Equal Opportunity Employer.***