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Applied Scientist II, Perimeter Protection Applied Science

Amazon.com Inc

  • Seattle, WA
  • 30+ days ago
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    Skills

    • Algorithmsunmatched
    • Amazon Web Services (AWS)unmatched
    • Analysis Skillsunmatched
    • Applications Securityunmatched
    • Artificial Intelligence (AI)unmatched
    • Cloud Computingunmatched
    • Data Managementunmatched
    • Data Modelingunmatched
    • Denial of Service (DoS)unmatched
    • Experiment Designunmatched
    • High Throughputunmatched
    • Internet Applicationunmatched
    • Internet Securityunmatched
    • Machine Learningunmatched
    • Model Validationunmatched
    • Modeling Languagesunmatched
    • Performance Modelingunmatched
    • Production Systemsunmatched
    • Scientific Methodunmatched
    • Security Analysisunmatched
    • Security Attacksunmatched
    • Software Engineeringunmatched
    • Training Data Setsunmatched
    • Web Programmingunmatched

    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.

    Numbers & Facts

    LocationSeattle, WA
    IndustryRetail
    Company Size10,000 employees or more
    Year Founded1994
    Websitehttp://Amazon.com/militaryroles

    About Company

    At Amazon, we don’t wait for the next big idea to present itself. We envision the shape of impossible things and then we boldly make them reality. So far, this mindset has helped us achieve some incredible things. Let’s build new systems, challenge the status quo, and design the world we want to live in. We believe the work you do here will be the best work of your life.

    Wherever you are in your career exploration, Amazon likely has an opportunity for you. Our research scientists and engineers shape the future of natural language understanding with Alexa. Fulfillment center associates around the globe send customer orders from our warehouses to doorsteps. Product managers set feature requirements, strategy, and marketing messages for brand new customer experiences. And as we grow, we’ll add jobs that haven’t been invented yet.

    It’s Always Day 1
    At Amazon, it’s always “Day 1.” Now, what does this mean and why does it matter? It means that our approach remains the same as it was on Amazon’s very first day – to make smart, fast decisions, stay nimble, invent, and stay focused on delighting our customers. In our 2016 shareholder letter, Amazon CEO Jeff Bezos shared his thoughts on how to keep up a Day 1 company mindset. “Staying in Day 1 requires you to experiment patiently, accept failures, plant seeds, protect saplings, and double down when you see customer delight,” he wrote. “A customer-obsessed culture best creates the conditions where all of that can happen.” You can read the full letter here

    Our Leadership Principles
    Our Leadership Principles help us keep a Day 1 mentality. They aren’t just a pretty inspirational wall hanging. Amazonians use them, every day, whether they’re discussing ideas for new projects, deciding on the best solution for a customer’s problem, or interviewing candidates. To read through our Leadership Principles from Customer Obsession to Bias for Action, visit https://www.amazon.jobs/principles

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