Senior AI Security & Robustness Engineer
Overview:
Keysight 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. 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 Keysight AI Labs
Keysight’s AI Labs is a global R\&D group pioneering the integration of machine learning, generative AIinto 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.
About the AI Team
Join *Keysight's central AI Hub in the heart of Barcelona.* We are expanding our newly formed AI Team.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.
About the Role
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.
Responsibilities:
This is a hands\-on and high\-impact role, blending applied research and production engineering:
- 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
- Secure ML artifacts via fingerprinting, obfuscation, and model watermarking
- Implement privacy\-preserving learning techniques (e.g., FL, DP\-SGD)
- Contribute to threat modeling and secure ML lifecycle governance
- Develop and maintain tooling for continuous robustness testing and secure MLOps workflows
- Collaborate with research and product teams to transition prototype defenses into production
- Publish and communicate findings internally and externally when appropriate
Required Qualifications
- Education: 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.
- Adversarial ML Expertise: Proven experience with model attacks, defenses, and robustness evaluation.
- Secure Deployment: Experience deploying hardened ML models to embedded or resource\-constrained environments.
- Secure ML Lifecycle: Familiarity with secure ML lifecycle management, threat modeling, and ML governance frameworks.
- Model IP Protection: Hands\-on experience with model watermarking, fingerprinting, and secure model storage.
- Frameworks \& Tools: Strong skills in PyTorch (preferred) or TensorFlow; familiarity with IBM ART, CleverHans, or similar security libraries.
- Privacy\-Preserving ML: Experience with DP\-SGD
- Strong communication and cross\-functional collaboration skills in English
- Experience with FL frameworks (e.g., Flower, OpenFL)
- Familiarity with cryptographic principles and secure computation techniques
- MLOps tooling experience (MLflow, W\&B, CI/CD)
- 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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