Senior Applied AI Scientist

Description About Us: Zenity is the first and only holistic platform built to secure and govern AI Agents from buildtime to runtime. We help organizations defend against security threats, meet compliance, and drive business productivity. Trusted by many of the world’s F500 companies, Zenity provides centralized visibility, vulnerability assessments, and governance by continuously scanning business-led development environments. We recently raised $38 million in a Series B funding, solidifying our position as a leader in the industry and enabling us to accelerate our mission of securing AI Agents everywhere. About the Job: We're looking for a Senior Applied AI Scientist to sit at the frontier of AI security - turning emerging threats into the detection models that protect how AI is used inside the world's largest organizations. You'll be part of the AI Security Research department, working hand-in-hand with security researchers to translate threat intelligence into trainable signals that catch malicious behavior and security risks across the AI-powered workflows of Fortune 500 companies. You'll bring deep technical versatility - reaching for classical ML, deep learning, or agentic based approaches based on what the problem demands, and the evaluation rigor to know when a model is truly ready for the real world. If you want to define what AI security engineering looks like, not just practice it, this role is for you. What You’ll Do Build, train, and ship detection models end-to-end, from raw data to production Choose the right method for each problem - traditional ML, deep learning, fine-tuned LLMs, agents or heuristics - based on theoretical insights turned into practical results. Partner with security researchers to turn security research outputs and domain expertise into detection capabilities Own evaluation: design benchmarks, build labeled datasets, and define production standards Monitor models in production across all paradigms - ML, deep learning, LLM-based, and agentic systems to track degradation and ensure reliability Iterate fast, with a tight feedback loop between model performance and product outcomes Requirements 5 years of hands-on ML and deep learning experience, with a track record of shipping, debugging, and diagnosing models in production Data-first mindset: you know how to define the right evaluation criteria for each model - before and after shipping, to ensure it delivers real quality and value in production Hands-on experience building and deploying agentic AI systems to production Proficiency in Python; experience with PyTorch, scikit-learn, HuggingFace, or equivalent Practical, applied mindset - focused on the problem, success metrics and impact, not lab research. Background in security, trust & safety, or content moderation - an advantage