Senior Machine Learning Engineer – Engineering Intelligence Systems

Senior Machine Learning Engineer – Engineering Intelligence Systems

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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.

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 Lang Graph 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.> /p

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.

This role sits at the intersection of machine learning, data engineering, and scientific modeling.

• Generalize across varying design and measurement scenarios.
• Learn from real and simulated data streams.
• Provide explainable and traceable predictions.
• Continuously improve performance and robustness through data‑driven refinement.

Goal:
Design and train ML models that capture engineering behaviors and physics‑based relationships.

• Develop predictive and surrogate models using experimental, simulation, and sensor data.
• Design feature representations and conditioning schemas that encode physical parameters, system constraints, and test configurations.
• Implement model pipelines capable of adapting to new devices, topologies, or domains with minimal retraining.
• Collaborate with domain engineers to align ML model design with real‑world measurement, calibration, and test semantics.

Goal:
Build robust data systems that convert engineering data into model‑ready intelligence.

• Develop data ingestion, transformation, and validation pipelines for structured, semi‑structured, and streaming data.
• Implement feedback loops where new simulation and measurement results automatically trigger data updates and retraining.
• Design augmentation and normalization strategies to enhance data diversity, reduce bias, and improve model stability.
• Ensure traceable data versioning and reproducibility, including detailed lineage and metadata tracking.

Goal:
Make engineering models transparent, interpretable, and auditable.

• Integrate Explainable AI (XAI) methods (e.g., SHAP, LIME, attention visualization, or gradient attribution) into model training and validation workflows.
• Develop diagnostic analytics dashboards to interpret model performance, bias, drift, and physical consistency.
• Create data and model introspection tools that allow engineers to inspect how features influence predictions.
• Establish confidence scoring and anomaly detection frameworks for model validation and trust in production applications.

• Expand machine learning models portfolio for…