Senior Machine Learning Engineer – Engineering Intelligence Systems

Keysight Technologies, Inc.•Calabasas, CA

About The Position

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 Initiative Keysight’s Applied AI Autonomy Initiative is developing a next-generation agentic orchestration framework that enables AI agents to reason, adapt, and coordinate across complex engineering workflows. Built on LangGraph and reinforcement-inspired feedback mechanisms, this framework transforms prompts and design intents into executable orchestration strategies that evolve autonomously through iterative simulation and validation loops. Our ambition is not merely to replicate human reasoning, but to push past human limits - enabling agentic systems to explore design spaces, optimize engineering workflows, and evolve orchestration strategies at a scale and speed no human could achieve. This effort moves beyond static model training — toward a continuous learning substrate where structured data, physics-informed features, and feedback signals refine model accuracy and generalization across complex engineering domains.

Requirements

PhD or 5+ years of experience in machine learning, applied data science, computational modeling, or related technical fields.
Strong foundation in computer science fundamentals (data structures, algorithms, and distributed systems) and their application to ML systems.
Proven experience developing neural or hybrid ML models for engineering, physics, or signal-processing domains.
Hands-on experience with data preprocessing, feature engineering, and pipeline automation (Python, SQL, or equivalent).
Proficiency in PyTorch, libtorch, or similar frameworks for model development and training.
Experience implementing XAI methods for scientific or engineering models.
Strong programming proficiency in Python, with experience in C++ integration for high-performance model components.
Experience using data management and analytics tools (e.g., pandas, NumPy, Apache Arrow, SQL).
Familiarity with experiment tracking and MLOps tools (e.g., MLflow, DVC, or equivalent).
Demonstrated ability to apply statistical analysis, uncertainty modeling, and visualization to engineering datasets.
Passion for building interpretable, data-driven models that explain — not just predict — engineering phenomena.

Nice To Haves

Background in scientific computing, simulation-driven modeling, or surrogate model development.
Familiarity with hybrid physical–statistical modeling techniques.
Experience with data fusion across multiple measurement or simulation sources.
Understanding of uncertainty quantification, sensitivity analysis, and confidence scoring in model evaluation.
Exposure to high-performance computing (HPC) or GPU-based model training environments.
Understanding of data base schema and SQL.

Responsibilities

Expand machine learning models portfolio for engineering and simulation-driven applications.
Improve and maintain data pipelines for model ingestion, feature extraction, and structured conditioning.
Implement explainability and performance diagnostics to ensure models remain interpretable and auditable.
Collaborate with simulation, measurement, and data science teams to align ML architectures with engineering use cases.
Continuously refine and validate models using real-world data feedback from measurement systems or simulation loops.