Senior Applied Scientist, Perimeter Protection Applied Science

Amazon Amazon · Big Tech · Seattle, WA · Machine Learning Science

Senior Applied Scientist role at AWS Perimeter Protection team, focusing on designing, building, and scaling AI-driven security solutions for AWS customers. The role involves the full ML lifecycle, from research to production deployment, with a focus on high-impact security challenges like WAF, DDoS, Bot Management, and Infrastructure Protection. It requires experience with production ML systems at scale, low-latency inference, and protecting services handling trillions of requests weekly.

What you'd actually do

  1. Develop and implement advanced AI/ML models and algorithms to enhance the capabilities of our security services, enabling proactive threat detection, mitigation, and protection against evolving cyber threats.
  2. Collaborate with security engineers and researchers, to understand business/domain requirements, analyze data patterns, and translate insights into actionable solutions.
  3. Design and drive implementation of data pipelines and ETL processes to ingest, process, and analyze large-scale security data from multiple sources, ensuring data quality and integrity.
  4. Conduct in-depth data analysis, feature engineering, and model evaluation to continuously improve the performance and accuracy of AI/ML-based security solutions.
  5. Participate in the development and deployment of AI/ML models into production environments, ensuring scalability, reliability, and performance at cloud scale.

Skills

Required

  • 3+ years of 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 in building machine learning models for business application

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 such as Hadoop, Spark etc.
  • Experience with large scale machine learning systems such as profiling and debugging and understanding of system performance and scalability
  • Experience in applied research
  • Experience with popular deep learning frameworks such as MxNet and Tensor Flow.
  • Experience using managed ML/AI solutions

What the JD emphasized

  • built and shipped production ML systems at industry scale
  • low-latency inference are critical

Other signals

  • build and ship production ML systems at industry scale
  • protect AWS customers worldwide
  • trillions of requests per week
  • low-latency inference