Machine Learning Engineer, AppStar Data Analytics & Engineering

The AppStar Data Analytics & Engineering (DNA) team within Amazon's Application Security organization is seeking a Machine Learning Engineer II to build, deploy, and scale ML systems that power how Amazon prioritizes and manages application security risk. You will own the end-to-end ML lifecycle: training and tuning models, building robust data pipelines, deploying models into production, and monitoring their performance at scale. Our team builds risk-scoring models, complexity-assessment systems, and graph-based intelligence layers that directly determine which applications receive security attention. This is hands-on engineering with direct, measurable impact on Amazon's security posture. The ideal candidate has strong software engineering fundamentals, experience deploying ML models in production environments, and a desire to work at the intersection of machine learning and security operations. You'll collaborate closely with applied scientists, data engineers, and security engineers to translate research into scalable, production-grade ML services on AWS. Key job responsibilities - Design, build, and maintain production ML pipelines (feature extraction, model training, scoring, serving) using AWS services such as SageMaker, Glue, S3, and Athena - Implement and deploy ML models for application security risk scoring, complexity assessment, and classification at scale - Develop scalable data processing systems that ingest, transform, and serve features from large-scale security datasets - Architect distributed computing solutions for model training and inference, leveraging Spark/PySpark and AWS infrastructure - Monitor model performance in production, implement drift detection, and drive continuous improvement through retraining and tuning - Partner with applied scientists to operationalize research prototypes into reliable, maintainable production services - Apply software engineering best practices: write testable, well-documented code, conduct peer reviews, and maintain operational excellence - Contribute to the team's ML infrastructure and tooling to improve development velocity and model reliability About the team The Data Analytics & Engineering (DNA) team is a small, high-impact group within Amazon's Application Security organization. We build ML models, graph-based systems, and analytics platforms that determine how Amazon prioritizes security coverage across tens of thousands of applications. We're builders who ship production ML, embrace ambiguity in messy security data, and operate with high ownership. Diverse Experiences Amazon Security values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Why Amazon Security? At Amazon, security is central to maintaining customer trust and delivering delightful customer experiences. Our organization is responsible for creating and maintaining a high bar for security across all of Amazon’s products and services. We offer talented security professionals the chance to accelerate their careers with opportunities to build experience in a wide variety of areas including cloud, devices, retail, entertainment, healthcare, operations, and physical stores. Inclusive Team Culture In Amazon Security, it’s in our nature to learn and be curious. Ongoing DEI events and learning experiences inspire us to continue learning and to embrace our uniqueness. Addressing the toughest security challenges requires that we seek out and celebrate a diversity of ideas, perspectives, and voices. Training & Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, training, and other career-advancing resources here to help you develop into a better-rounded