Senior Machine Learning Engineer - Engineering Intelligence SystemsKeysight Technologies • Calabasas, California, United States
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Senior Machine Learning Engineer - Engineering Intelligence Systems
Keysight Technologies
- Calabasas, California, United States
- Calabasas, California, United States
About
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.
Core Responsibility Domains Engineering Model Creation & Neural Conditioning 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.
Data Intelligence, Feedback & Augmentation 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.
Explainable AI & Diagnostic Analytics 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.
Key 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.
Qualifications Required Qualifications
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 automationPython, SQL, or equivalent). Proficiency in PyTorch, libtorch, or similar frameworks for model development and training. Experience implementing XAI methods for scientific or engineering models.
Preferred Qualifications
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 database schema and SQL.
Prerequisites
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.
What This Role Offers
A defining opportunity to build the machine learning foundation that powers Keysight’s next‑generation of engineering and simulation intelligence. The chance to design adaptive, explainable models that 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 across simulation, AI, modeling, and measurement science, translating rich engineering data into transparent, high‑assurance intelligence. A role where your work directly accelerates Keysight’s shift toward self‑improving engineering models and continuous learning pipelines.
The 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. CA pay range: MIN $156,740- MAX $261,230 Note: For other locations, pay ranges will vary by region. This role is eligible for our Keysight Results Bonus Program. Benefits
Medical, dental and vision Health Savings Account Health Care and Dependent Care Flexible Spending Accounts Life, Accident, Disability insurance Business Travel Accident and Business Travel Health 401(k) Plan Flexible Time Off, Paid Holidays Paid Family Leave Discounts, Perks Tuition Reimbursement Adoption Assistance ESPP (Employee Stock Purchase Plan) Restricted Stock Units
Seniority level Not Applicable Employment type Full‑time Job function Engineering and Information Technology Industries Appliances, Electrical, and Electronics Manufacturing Keysight is an Equal Opportunity Employer. Keysight 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. #J-18808-Ljbffr
Languages
- English
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