We are seeking a talented Applied Scientist to join Amazon's Traffic Engineering organization in developing next-generation bot detection and mitigation capabilities. You will contribute to the scientific development of real-time anomaly detection systems to identify and counter sophisticated automated threats, including LLM-powered agents, protecting Amazon's websites and subsidiaries worldwide.
This is a critical challenge at the intersection of ML and cybersecurity. With GenAI/LLM-powered bots now capable of mimicking human behavior, evading traditional detections, and monetizing attacks, we must revolutionize our approach from reactive to real-time protection. You'll help build ML systems that can detect threats in milliseconds instead of days, protecting Amazon assets and compute while ensuring legitimate bot traffic continues to benefit our business. This role offers the unique opportunity to implement first-of-its-kind real-time behavior-based ML models that will safeguard Amazon's global infrastructure. Your work will directly impact millions of customers by ensuring the integrity and availability of Amazon's digital presence while advancing the state-of-the-art in automated threat detection and response.
Key job responsibilities
* Implement and optimize ML models for real-time detection of programmatic access patterns and anomalous behaviors
* Support development of new approaches in identifying LLM-based agents and emerging automated threats
* Apply ML/AI techniques to traffic analysis, classification, and automated response systems
* Work with cross-functional teams to implement layered defense strategies
* Contribute to the team's technical implementations and documentation
