CYBER SECURITY SENIOR ENGINEER

is on 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. Our

culture embraces a bold vision of where technology can take us and a passion for tackling challenging problems with industry-first solutions. About Keysight AI Labs Keysight’s

AI Labs

is a global R&D group pioneering the integration of

into Keysight’s test, measurement, and design solutions. Our mission is to transform how engineers design, simulate, and validate advanced systems- from 6G and semiconductors to quantum and automotive - by embedding AI throughout our workflows.

As part of this growing team, you will join a vibrant, cross-functional environment that brings together experts in ML engineering, data science, physics-informed modeling, and software development. You’ll work closely with domain experts across RF, EM, circuit design, and test & measurement to accelerate scientific innovation through AI.

We are seeking a

Senior ML Security & Robustness Engineer

who will lead the design and deployment of secure and resilient ML systems. This is a hands-on, research-informed engineering role focused on

adversarial robustness, secure training, and model lifecycle security

across diverse deployment targets, on-device, hybrid, edge, and cloud.

You will collaborate with applied researchers, data scientists, and infrastructure teams to design ML security solutions that scale from lab prototypes to enterprise-grade deployments.

Design, test, and deploy adversarial defenses for ML models across varied deployment architectures (edge, hybrid, cloud) Own robustness evaluation pipelines, red-teaming, and model penetration testing Develop and maintain tooling for continuous robustness testing and secure MLOps workflows Master’s or PhD in Computer Science, Electrical Engineering, Applied Mathematics, Cybersecurity, or related field. ML/DL Foundations:

Deep understanding of neural networks, optimization, and statistical learning theory. Secure Deployment:

Frameworks & Tools:

Strong skills in

PyTorch

(preferred) or TensorFlow; familiarity with

IBM ART, CleverHans

, or similar security libraries. Strong communication and cross-functional collaboration skills in English

Publications in top AI and/or security venues (NeurIPS, ICML, AAAI, IEEE S&P, USENIX, ACM CCS, etc.) Contributions to open-source ML security projects

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