Senior Applied Scientist, Perimeter Protection Applied Science

Join the AWS Perimeter Protection team as a Senior Applied Scientist, where you will bring your deep ML engineering expertise to design, build, and scale AI-driven security solutions that protect AWS customers worldwide. This role is ideal for someone who has already built and shipped production ML systems at industry scale and is looking to apply that experience to high-impact security challenges. You will own the full ML lifecycle - from research and prototyping to production deployment and optimization -

powering services including Web Application Firewall, DDoS Protection, Bot Management, and Infrastructure Protection. With services spanning all AWS regions and handling trillions of requests per week, you will solve complex engineering and science problems where model performance, system reliability, and low-latency inference are critical.

Key job responsibilities

• Design, build, and deploy production-grade ML models and systems for real-time threat detection, mitigation, and protection against evolving cyber threats at cloud scale.
• Own the full ML lifecycle end-to-end - from problem formulation, data engineering, and model development through to production deployment, monitoring, and continuous

improvement.

• Architect and optimize ML pipelines, training infrastructure, and serving systems to meet strict latency, throughput, and reliability requirements at AWS scale.
• Bridge the gap between research and production by translating novel ML approaches into robust, scalable, and maintainable systems that operate in real-time security environments.
• Design and implement feature engineering workflows and large-scale data processing pipelines to support rapid experimentation and reliable model iteration.
• Collaborate closely with software engineering teams to integrate ML models into distributed, low-latency security services, driving engineering decisions around model serving, infrastructure, and system design.
• Analyze large-scale production data to identify patterns, anomalies, and emerging threat vectors, and translate findings into measurable improvements to detection and mitigation capabilities.
• Establish and improve best practices for ML system design, model evaluation, A/B testing, and production monitoring across the team.
• Mentor junior scientists and engineers, raising the bar on both scientific rigor and engineering quality.