Senior AI Researcher

About Us:  Zenity is the leader in AI Agent Security and the first company to bring an agent-centric security platform to market. As enterprises accelerate AI agent adoption, we are establishing the security framework for how AI agents are secured and governed at enterprise scale. We deliver full-lifecycle visibility, governance, detection, prevention, and response for AI agents from build time to runtime, across SaaS, home-grown platforms, and end-user devices. Backed by $180M+ in total funding, including a $125M Series C led by Norwest, with participation from SoftBank Vision Fund 2 and Microsoft's M12, Zenity is trusted by Fortune 500 and Global 2000 enterprises worldwide. Join us in shaping how AI agents are secured at enterprise scale. About the Role: This is a research‑first role focused on deeply understanding LLM internals to improve the security of AI agents. You’ll design careful experiments on activations and interpretable features- e.g., probing, attribution & ablation/patching, representation‑geometry analyses-to uncover mechanisms behind jailbreak, indirect prompt injection, and other attacks. Then translate those insights into signals that can be used for detection and analysis of a model response. The field of LLM interpretability at scale is exploding, with several major publications in the last months, and major opportunities for innovation.

Requirements: Requirements: • Deep learning expertise with a track record of non‑trivial research (industry or academia) in LLMs or other domains (e.g., CV, speech). We care that you’ve changed models or methods in meaningful ways (architecture/training/eval), not just used them. • Strong experimental design and scientific writing; comfort pre‑registering hypotheses, testing causal claims, proposing novel directions in a fast-changing field. • PhD or equivalent research experience in the industry (5+ years in a leading research team). Publication record or a portfolio of high‑impact open artifacts will make you stand out from the crowd. • Familiarity with AI frameworks (e.g., HuggingFace Transformers, LangChain, scikit-learn, PyTorch); Experience with a production grade codebase with several contributors is a bonus. • Experience in data analysis: visualization, exploration, cleanup. • Knowledge in GenAI tools such as LLM Orchestrations and integration packages, Agents, RAG systems - a bonus.