Senior Architect - Agentic Orchestration Frameworks

This role sits at the intersection ofmachine learning, data engineering, and scientific modeling. You will build themodel intelligence and feedback infrastructurethat allows engineering models to:

• Generalize across varying design and measurement scenarios

• Learn from real and simulated data streams

• Provideexplainable and traceable predictions

• Continuously improve performance and robustness through data-driven refinement The ideal candidate has a strong foundation inapplied machine learning,scientific data analysis, andmodel interpretability, designing adaptive data systems where engineering models evolve intelligently over time. Core Responsibility Domains

• Engineering Model Creation & Neural Conditioning Goal:Design and train ML models that capture engineering behaviors and physics-based relationships.

• Developpredictive and surrogate modelsusing experimental, simulation, and sensor data.

• Designfeature representations and conditioning schemasthat encode physical parameters, system constraints, and test configurations.

• Implementmodel pipelinescapable 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.

• Data Intelligence, Feedback & Augmentation Goal:Build robust data systems that convert engineering data into model-ready intelligence.

• Developdata ingestion, transformation, and validation pipelinesfor structured, semi-structured, and streaming data.

• Implementfeedback loopswhere new simulation and measurement results automatically trigger data updates and retraining.

• Designaugmentation and normalization strategiesto enhance data diversity, reduce bias, and improve model stability.

• Ensuretraceable data versioning and reproducibility, including detailed lineage and metadata tracking.

• Explainable AI & Diagnostic Analytics Goal:Make engineering models transparent, interpretable, and auditable.

• IntegrateExplainable AI (XAI)methods (e.g., SHAP, LIME, attention visualization, or gradient attribution) into model training and validation workflows.

• Developdiagnostic analytics dashboardsto interpret model performance, bias, drift, and physical consistency.

• Createdata and model introspection toolsthat allow engineers to inspect how features influence predictions.

• Establishconfidence scoring and anomaly detection frameworksfor model validation and trust in production applications., * Expandmachine learning models portfoliofor engineering and simulation-driven applications.

• Improve and maintaindata pipelinesfor model ingestion, feature extraction, and structured conditioning.

• Implementexplainability and performance diagnosticsto ensure models remain interpretable and auditable.

• Collaborate with simulation, measurement, and data science teams toalign ML architectures with engineering use cases.

• Continuously refine and validate models usingreal-world data feedbackfrom measurement systems or simulation loops. What This Role Offers

• A defining opportunity to build the machine learning foundationthat powers Keysight's next generation of engineering and simulation intelligence.

• The chance to designadaptive, explainable modelsthat learn from complex measurement, simulation, and telemetry data - capturing real-world system behavior with scientific rigor.

• Direct impact on the architecture and evolution of scientific ML systems, shaping how engineering decisions are modeled, predicted, optimized, and explained.

• Deep collaboration with leading experts acrosssimulation, AI, modeling, and measurement science, translating rich engineering data intotransparent, high-assurance intelligence.

• A role where your work directly accelerates Keysight's shift towardself-improving engineering models and continuous learning pipelines.

• PhD or5+ years of experienceinmachine learning,applied data science,computational modeling, or related technical fields.

• Strong foundation incomputer science fundamentals(data structures, algorithms, and distributed systems) and their application to ML systems.

• Proven experience developingneural or hybrid ML modelsfor engineering, physics, or signal-processing domains.

• Hands-on experience withdata preprocessing, feature engineering, and pipeline automation(Python, SQL, or equivalent).

• Proficiency inPyTorch,libtorch, or similar frameworks for model development and training.

• Experience implementingXAI methodsfor scientific or engineering models. Preferred Qualifications

• Background inscientific computing,simulation-driven modeling, orsurrogate model development.

• Familiarity with hybrid physical-statistical modeling techniques.

• Experience withdata fusionacross multiple measurement or simulation sources.

• Understanding ofuncertainty quantification,sensitivity analysis, andconfidence scoringin model evaluation.

• Exposure tohigh-performance computing (HPC)or GPU-based model training environments.

• Understanding of data base schema and SQL. Prerequisites

• Strong programming proficiency inPython, with experience inC++ integrationfor high-performance model components.

• Experience usingdata management and analytics tools(e.g., pandas, NumPy, Apache Arrow, SQL).

• Familiarity withexperiment tracking and MLOps tools(e.g., MLflow, DVC, or equivalent).

• Demonstrated ability to applystatistical analysis, uncertainty modeling, and visualizationto engineering datasets.

• Passion for buildinginterpretable, data-driven modelsthat explain - not just predict - engineering phenomena.

Keysight is at the forefront of technology innovation, delivering breakthroughs and trusted insights in electronic design, simulation, prototyping, test, manufacturing, and optimization. Our ~15, employees create world-class solutions in communications, 5G, automotive, energy, quantum, aerospace, defense, and semiconductor markets for customers in over countries. Learn more about what we do. Our award-winningculture 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'sApplied AI Autonomy Initiativeis developing a next-generationagentic orchestration frameworkthat enables AI agents to reason, adapt, and coordinate across complex engineering workflows. Built onLangGraphandreinforcement-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 topush 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. The goal is to create thefoundational runtime for adaptive, multi-agent reasoning at scale, where AI systems not on