Senior Machine Learning Engineer – Engineering Intelligence Systems

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, 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’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.

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

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

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

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

Key Responsibilities

Qualifications

Required Qualifications

Preferred Qualifications

Prerequisites

What This Role Offers

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 $,- MAX $,

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 :

Careers Privacy Statement

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.