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 Youll 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.
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.
