Sr. Applied Scientist, Enterprise Security Products

Amazon Amazon · Big Tech · Austin, TX · Applied Science

Senior Applied Scientist role focused on building AI-first security products. The role involves defining the science vision, inventing and building novel ML solutions (including agentic architectures and RAG systems), tackling ambiguous security challenges, and shipping end-to-end solutions. It requires staying ahead of advancements in foundation models and agentic AI, and influencing across the organization. The role emphasizes research rigor, rapid prototyping, and influencing the team's culture and scientific practices.

What you'd actually do

  1. Set the science vision: Define a multi-year science roadmap for AI-powered security products. Identify where foundation models, agentic systems, and emerging ML techniques can fundamentally change how we detect, prevent, and respond to threats protecting Amazon Business customers.
  2. Invent and build, end to end: Design and personally develop novel ML solutions — from agentic architectures and RAG systems to anomaly detection and behavior modeling. You won't hand off prototypes; you'll partner with engineers and PMs to ship them into production.
  3. Tackle scientifically ambiguous problems: Translate messy, open-ended security challenges into well-defined scientific problems. Own the critical novelty in our systems and provide system-wide design guidance.
  4. Experiment with rigor and speed: Design and run offline and online experiments to validate ideas quickly. Make data-driven calls on what ships, what iterates, and what gets killed.
  5. Push the frontier: Stay ahead of advancements in foundation models, agentic AI, and applied ML. Evaluate emerging capabilities and make strategic build-vs-buy decisions. Publish your work at top conferences when it advances the field.

Skills

Required

  • building machine learning models for business application experience
  • PhD, or Master's degree and 6+ years of applied research experience
  • Experience programming in Java, C++, Python or related language
  • Experience with neural deep learning methods and machine learning
  • Experience communicating complex ideas to technical and non-technical audiences

Nice to have

  • Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
  • Experience with large scale distributed systems

What the JD emphasized

  • ship it to production
  • agentic AI
  • foundation models
  • novel ML approaches
  • ship them into production
  • scientifically ambiguous problems
  • emerging capabilities
  • publish your work at top conferences
  • building machine learning models for business application experience
  • applied research experience

Other signals

  • building intelligent, cloud-agnostic security tools using AI-first development practices
  • agentic AI, foundation models, and novel ML approaches aren't future bets, they're what we're building right now
  • define the scientific direction of a greenfield security portfolio
  • shape the future of security tooling with a small, fast team that researches like a lab and deploys like Amazon