Back to all jobs
A
Applied Scientist II, Perimeter Protection Applied Science
Seattle, Washington, USA
full-timeData ScienceJob Description
Join the AWS Perimeter Protection team as an Applied Scientist, where you will design and build AI/ML models that protect AWS customers from cyber threats at massive scale.
You will work on challenging problems in threat detection, bot management, DDoS protection, and web application security — developing and deploying machine learning solutions that leverage techniques including large language models, generative AI, and agentic AI systems. Operating across all AWS regions and processing trillions of requests per week, you will collaborate with experienced scientists and engineers to deliver production-grade, intelligent security systems that provide robust, adaptive, and
forward-looking protection for AWS customers worldwide.
Key job responsibilities
- Design, develop, and evaluate ML models and algorithms for threat detection, anomaly detection, and mitigation of evolving cyber threats including DDoS attacks, bot activity, and web application exploits.
- Explore and apply large language models, generative AI, and agentic AI approaches to security challenges such as automated threat analysis, intelligent mitigation, and
adaptive defense systems.
- Implement end-to-end ML solutions — from data exploration and feature engineering through model training, evaluation, and deployment into production systems.
- Analyze large-scale datasets to uncover patterns, identify emerging threat vectors, and translate findings into effective ML-based security solutions.
- Build and maintain data pipelines and model training workflows that support rapid experimentation and reliable production performance.
- Collaborate with software engineers to integrate ML models into low-latency, high-throughput security systems at cloud scale.
- Design and run experiments to validate model performance, measure impact, and iterate on approaches using rigorous scientific methodology.
- Stay current with recent advances in AI/ML — including LLMs, generative AI, and agentic systems — and cybersecurity research, applying relevant techniques to improve detection and protection capabilities.
- Contribute to design reviews, and knowledge sharing.
- Participate in the team's scientific roadmap by proposing ideas and identifying opportunities to improve existing systems.
You will work on challenging problems in threat detection, bot management, DDoS protection, and web application security — developing and deploying machine learning solutions that leverage techniques including large language models, generative AI, and agentic AI systems. Operating across all AWS regions and processing trillions of requests per week, you will collaborate with experienced scientists and engineers to deliver production-grade, intelligent security systems that provide robust, adaptive, and
forward-looking protection for AWS customers worldwide.
Key job responsibilities
- Design, develop, and evaluate ML models and algorithms for threat detection, anomaly detection, and mitigation of evolving cyber threats including DDoS attacks, bot activity, and web application exploits.
- Explore and apply large language models, generative AI, and agentic AI approaches to security challenges such as automated threat analysis, intelligent mitigation, and
adaptive defense systems.
- Implement end-to-end ML solutions — from data exploration and feature engineering through model training, evaluation, and deployment into production systems.
- Analyze large-scale datasets to uncover patterns, identify emerging threat vectors, and translate findings into effective ML-based security solutions.
- Build and maintain data pipelines and model training workflows that support rapid experimentation and reliable production performance.
- Collaborate with software engineers to integrate ML models into low-latency, high-throughput security systems at cloud scale.
- Design and run experiments to validate model performance, measure impact, and iterate on approaches using rigorous scientific methodology.
- Stay current with recent advances in AI/ML — including LLMs, generative AI, and agentic systems — and cybersecurity research, applying relevant techniques to improve detection and protection capabilities.
- Contribute to design reviews, and knowledge sharing.
- Participate in the team's scientific roadmap by proposing ideas and identifying opportunities to improve existing systems.