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 LabsKeysight's AI Labs is a general 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 testingDevelop and maintain tooling for continuous robustness testing and secure MLOps workflowsMaster'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 EnglishPublications in top AI and/or security venues (NeurIPS, ICML, AAAI, IEEE S&P;, USENIX, ACM CCS, etc.)Contributions to open-source ML security projects***