R&D Engineer

Overview:

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

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.

Responsibilities:

Role Overview

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

You will build the model intelligence and feedback infrastructure that allows engineering models to:

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

The 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.

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 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 3+ 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.

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 data base 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 $122,580- MAX $199,340

Note: For other locations, pay ranges will vary by region

This role is eligible for our Keysight Results Bonus Program

US Employees may be eligible for the following 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

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