ML Researcher, Autonomous Security

About the role

The ML Researcher will conduct pioneering research in streaming provenance-based intrusion detection systems (Prov-IDS), leveraging advanced machine learning, deep learning, and related AI fields. This role will focus on designing and implementing novel approaches for fine-grained, process-level threat detection over real-time event streams, specifically utilizing provenance graphs. You will be responsible for developing and evaluating iterative embedding techniques using sequential neural networks (e.g., RNNs, GRUs) that can process entire provenance graphs while consuming a fraction of the computational and memory costs associated with traditional Graph Neural Networks (GNNs). Your research will address critical challenges such as memory overhead, detection lag, mimicry attacks, and concept drift, providing roadmaps, prototypes, and algorithms for autonomous agents in low-resource, on-device, and distributed environments. This position requires a deep understanding of the challenges and opportunities in applying advanced ML to real-world cybersecurity scenarios, including scalability, interpretability, robustness, privacy, and ethical considerations.