Senior ML Engineer – Scientific & Engineering Data

Overview:

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