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 Youll 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
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:
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
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
This position is open to all candidates.
