ML Security & Robustness Engineer
Role details
Job location
Tech stack
Job description
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
Requirements
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