Senior Machine Learning Engineer – Engineering Intelligence Systems
Keysight Technologies · Calabasas, CA · R&D
About this role
Keysight Technologies is hiring a senior-level Machine Learning Engineer based in Calabasas, CA. The posting calls out experience with Python, SQL, PyTorch, pandas. Listed education preference: a bachelor's degree or equivalent.
- Role
- Machine Learning Engineer
- Function
- machine learning
- Level
- senior
- Track
- Individual contributor
- Employment
- Full-time
- Location
- Calabasas, CA
- Education
- Bachelor's degree
- Department
- R&D
- Posted
- Dec 23, 2025
More roles at Keysight Technologies
Job description
from Keysight Technologies careersKeysight 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
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.