Senior Applied AI Scientist

Description

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

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

Responsibilities

• 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

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