{"schemaVersion":"jobsearcher.job.v1","id":"6ffe5dca17eafe11be21a45a","url":"https://jobsearcher.com/jobs/6ffe5dca17eafe11be21a45a","canonicalUrl":"https://jobsearcher.com/jobs/6ffe5dca17eafe11be21a45a","title":"Senior Architect – Agentic Orchestration Frameworks","description":"Overview:\nKeysight 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.\n\nOur 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.\n\nAbout the Initiative\nKeysight’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.\nOur 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.\nThe goal is to create the foundational runtime for adaptive, multi-agent reasoning at scale, where AI systems not only execute tasks but collaborate, refine, and self-improve across engineering domains.\nResponsibilities:\nRole Overview\nThis role sits at the intersection of machine learning, data engineering, and scientific modeling.\nYou will build the model intelligence and feedback infrastructure that allows engineering models to:\nGeneralize across varying design and measurement scenarios\nLearn from real and simulated data streams\nProvide explainable and traceable predictions\nContinuously improve performance and robustness through data-driven refinement\nThe ideal candidate has a strong foundation in applied machine learning, scientific data analysis, and model interpretability, designing adaptive data systems where engineering models evolve intelligently over time.\nCore Responsibility Domains\nEngineering Model Creation & Neural Conditioning\nGoal: Design and train ML models that capture engineering behaviors and physics-based relationships.\nDevelop predictive and surrogate models using experimental, simulation, and sensor data.\nDesign feature representations and conditioning schemas that encode physical parameters, system constraints, and test configurations.\nImplement model pipelines capable of adapting to new devices, topologies, or domains with minimal retraining.\nCollaborate with domain engineers to align ML model design with real-world measurement, calibration, and test semantics.\nData Intelligence, Feedback & Augmentation\nGoal: Build robust data systems that convert engineering data into model-ready intelligence.\nDevelop data ingestion, transformation, and validation pipelines for structured, semi-structured, and streaming data.\nImplement feedback loops where new simulation and measurement results automatically trigger data updates and retraining.\nDesign augmentation and normalization strategies to enhance data diversity, reduce bias, and improve model stability.\nEnsure traceable data versioning and reproducibility, including detailed lineage and metadata tracking.\nExplainable AI & Diagnostic Analytics\nGoal: Make engineering models transparent, interpretable, and auditable.\nIntegrate Explainable AI (XAI) methods (e.g., SHAP, LIME, attention visualization, or gradient attribution) into model training and validation workflows.\nDevelop diagnostic analytics dashboards to interpret model performance, bias, drift, and physical consistency.\nCreate data and model introspection tools that allow engineers to inspect how features influence predictions.\nEstablish confidence scoring and anomaly detection frameworks for model validation and trust in production applications.\nKey Responsibilities\nExpand machine learning models portfolio for engineering and simulation-driven applications.\nImprove and maintain data pipelines for model ingestion, feature extraction, and structured conditioning.\nImplement explainability and performance diagnostics to ensure models remain interpretable and auditable.\nCollaborate with simulation, measurement, and data science teams to align ML architectures with engineering use cases.\nContinuously refine and validate models using real-world data feedback from measurement systems or simulation loops.\n\nWhat This Role Offers\nA defining opportunity to build the machine learning foundation that powers Keysight’s next generation of engineering and simulation intelligence.\nThe chance to design adaptive, explainable models that learn from complex measurement, simulation, and telemetry data — capturing real-world system behavior with scientific rigor.\nDirect impact on the architecture and evolution of scientific ML systems, shaping how engineering decisions are modeled, predicted, optimized, and explained.\nDeep collaboration with leading experts across simulation, AI, modeling, and measurement science, translating rich engineering data into transparent, high-assurance intelligence.\nA role where your work directly accelerates Keysight’s shift toward self-improving engineering models and continuous learning pipelines.\nQualifications:\n\nRequired Qualifications\nPhD or 5+ years of experience in machine learning, applied data science, computational modeling, or related technical fields.\nStrong foundation in computer science fundamentals (data structures, algorithms, and distributed systems) and their application to ML systems.\nProven experience developing neural or hybrid ML models for engineering, physics, or signal-processing domains.\nHands-on experience with data preprocessing, feature engineering, and pipeline automation (Python, SQL, or equivalent).\nProficiency in PyTorch, libtorch, or similar frameworks for model development and training.\nExperience implementing XAI methods for scientific or engineering models.\nPreferred Qualifications\nBackground in scientific computing, simulation-driven modeling, or surrogate model development.\nFamiliarity with hybrid physical–statistical modeling techniques.\nExperience with data fusion across multiple measurement or simulation sources.\nUnderstanding of uncertainty quantification, sensitivity analysis, and confidence scoring in model evaluation.\nExposure to high-performance computing (HPC) or GPU-based model training environments.\nUnderstanding of data base schema and SQL.\nPrerequisites\nStrong programming proficiency in Python, with experience in C++ integration for high-performance model components.\nExperience using data management and analytics tools (e.g., pandas, NumPy, Apache Arrow, SQL).\nFamiliarity with experiment tracking and MLOps tools (e.g., MLflow, DVC, or equivalent).\nDemonstrated ability to apply statistical analysis, uncertainty modeling, and visualization to engineering datasets.\nPassion for building interpretable, data-driven models that explain — not just predict — engineering phenomena.\nThe level of role will be based on applicable experience, education and skills; Most offers will be between the minimum and the midpoint of the Salary Range listed below.\n\nCA pay range: MIN $156,740- MAX $261,230\n\nNote: For other locations, pay ranges will vary by region\nThis role is eligible for our Keysight Results Bonus Program\nUS Employees may be eligible for the following benefits:\nMedical, dental and vision\nHealth Savings Account\nHealth Care and Dependent Care Flexible Spending Accounts\nLife, Accident, Disability insurance\nBusiness Travel Accident and Business Travel Health\n401(k) Plan\nFlexible Time Off, Paid Holidays\nPaid Family Leave\nDiscounts, Perks\nTuition Reimbursement\nAdoption Assistance\nESPP (Employee Stock Purchase Plan)\nRestricted Stock Units\n\nCareers Privacy Statement\nKeysight is an Equal Opportunity Employer\n\nKeysight Technologies Inc. is an equal opportunity employer. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability or any other protected categories under all applicable laws.","company":"Keysighttechnologies","rawCompany":"keysighttechnologies","city":"Calabasas","state":"CA","isRemote":false,"isActive":false,"createdAt":"2026-07-16T17:59:26.384Z","occupations":[{"code":"15-1299.08","title":"Computer Systems Engineers/Architects","slug":"computer-systems-engineers-architects"},{"code":"17-2199.00","title":"Engineers, All Other","slug":"engineers-all-other"},{"code":"11-9041.00","title":"Architectural and Engineering Managers","slug":"architectural-and-engineering-managers"}],"industries":[{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"},{"code":"541330","title":"Engineering Services","slug":"engineering-services"},{"code":"541715","title":"Research and Development in the Physical, Engineering, and Life Sciences (except Nanotechnology and Biotechnology)","slug":"research-and-development-in-the-physical-engineering-and-life-sciences-except-nanotechnology-and-biotechnology"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Senior Architect – Agentic Orchestration Frameworks","description":"Overview:\nKeysight 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.\n\nOur 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.\n\nAbout the Initiative\nKeysight’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.\nOur 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.\nThe goal is to create the foundational runtime for adaptive, multi-agent reasoning at scale, where AI systems not only execute tasks but collaborate, refine, and self-improve across engineering domains.\nResponsibilities:\nRole Overview\nThis role sits at the intersection of machine learning, data engineering, and scientific modeling.\nYou will build the model intelligence and feedback infrastructure that allows engineering models to:\nGeneralize across varying design and measurement scenarios\nLearn from real and simulated data streams\nProvide explainable and traceable predictions\nContinuously improve performance and robustness through data-driven refinement\nThe ideal candidate has a strong foundation in applied machine learning, scientific data analysis, and model interpretability, designing adaptive data systems where engineering models evolve intelligently over time.\nCore Responsibility Domains\nEngineering Model Creation & Neural Conditioning\nGoal: Design and train ML models that capture engineering behaviors and physics-based relationships.\nDevelop predictive and surrogate models using experimental, simulation, and sensor data.\nDesign feature representations and conditioning schemas that encode physical parameters, system constraints, and test configurations.\nImplement model pipelines capable of adapting to new devices, topologies, or domains with minimal retraining.\nCollaborate with domain engineers to align ML model design with real-world measurement, calibration, and test semantics.\nData Intelligence, Feedback & Augmentation\nGoal: Build robust data systems that convert engineering data into model-ready intelligence.\nDevelop data ingestion, transformation, and validation pipelines for structured, semi-structured, and streaming data.\nImplement feedback loops where new simulation and measurement results automatically trigger data updates and retraining.\nDesign augmentation and normalization strategies to enhance data diversity, reduce bias, and improve model stability.\nEnsure traceable data versioning and reproducibility, including detailed lineage and metadata tracking.\nExplainable AI & Diagnostic Analytics\nGoal: Make engineering models transparent, interpretable, and auditable.\nIntegrate Explainable AI (XAI) methods (e.g., SHAP, LIME, attention visualization, or gradient attribution) into model training and validation workflows.\nDevelop diagnostic analytics dashboards to interpret model performance, bias, drift, and physical consistency.\nCreate data and model introspection tools that allow engineers to inspect how features influence predictions.\nEstablish confidence scoring and anomaly detection frameworks for model validation and trust in production applications.\nKey Responsibilities\nExpand machine learning models portfolio for engineering and simulation-driven applications.\nImprove and maintain data pipelines for model ingestion, feature extraction, and structured conditioning.\nImplement explainability and performance diagnostics to ensure models remain interpretable and auditable.\nCollaborate with simulation, measurement, and data science teams to align ML architectures with engineering use cases.\nContinuously refine and validate models using real-world data feedback from measurement systems or simulation loops.\n\nWhat This Role Offers\nA defining opportunity to build the machine learning foundation that powers Keysight’s next generation of engineering and simulation intelligence.\nThe chance to design adaptive, explainable models that learn from complex measurement, simulation, and telemetry data — capturing real-world system behavior with scientific rigor.\nDirect impact on the architecture and evolution of scientific ML systems, shaping how engineering decisions are modeled, predicted, optimized, and explained.\nDeep collaboration with leading experts across simulation, AI, modeling, and measurement science, translating rich engineering data into transparent, high-assurance intelligence.\nA role where your work directly accelerates Keysight’s shift toward self-improving engineering models and continuous learning pipelines.\nQualifications:\n\nRequired Qualifications\nPhD or 5+ years of experience in machine learning, applied data science, computational modeling, or related technical fields.\nStrong foundation in computer science fundamentals (data structures, algorithms, and distributed systems) and their application to ML systems.\nProven experience developing neural or hybrid ML models for engineering, physics, or signal-processing domains.\nHands-on experience with data preprocessing, feature engineering, and pipeline automation (Python, SQL, or equivalent).\nProficiency in PyTorch, libtorch, or similar frameworks for model development and training.\nExperience implementing XAI methods for scientific or engineering models.\nPreferred Qualifications\nBackground in scientific computing, simulation-driven modeling, or surrogate model development.\nFamiliarity with hybrid physical–statistical modeling techniques.\nExperience with data fusion across multiple measurement or simulation sources.\nUnderstanding of uncertainty quantification, sensitivity analysis, and confidence scoring in model evaluation.\nExposure to high-performance computing (HPC) or GPU-based model training environments.\nUnderstanding of data base schema and SQL.\nPrerequisites\nStrong programming proficiency in Python, with experience in C++ integration for high-performance model components.\nExperience using data management and analytics tools (e.g., pandas, NumPy, Apache Arrow, SQL).\nFamiliarity with experiment tracking and MLOps tools (e.g., MLflow, DVC, or equivalent).\nDemonstrated ability to apply statistical analysis, uncertainty modeling, and visualization to engineering datasets.\nPassion for building interpretable, data-driven models that explain — not just predict — engineering phenomena.\nThe level of role will be based on applicable experience, education and skills; Most offers will be between the minimum and the midpoint of the Salary Range listed below.\n\nCA pay range: MIN $156,740- MAX $261,230\n\nNote: For other locations, pay ranges will vary by region\nThis role is eligible for our Keysight Results Bonus Program\nUS Employees may be eligible for the following benefits:\nMedical, dental and vision\nHealth Savings Account\nHealth Care and Dependent Care Flexible Spending Accounts\nLife, Accident, Disability insurance\nBusiness Travel Accident and Business Travel Health\n401(k) Plan\nFlexible Time Off, Paid Holidays\nPaid Family Leave\nDiscounts, Perks\nTuition Reimbursement\nAdoption Assistance\nESPP (Employee Stock Purchase Plan)\nRestricted Stock Units\n\nCareers Privacy Statement\nKeysight is an Equal Opportunity Employer\n\nKeysight Technologies Inc. is an equal opportunity employer. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability or any other protected categories under all applicable laws.","datePosted":"2026-07-16T17:59:26.384Z","dateModified":"2026-07-16T17:59:26.384Z","hiringOrganization":{"@type":"Organization","name":"Keysighttechnologies","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Calabasas","addressRegion":"CA","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"6ffe5dca17eafe11be21a45a"},"url":"https://jobsearcher.com/jobs/6ffe5dca17eafe11be21a45a"}}