Amazon
Applied Scientist, Leo Security
$143K–$193K
Arlington, VA +2 · Full-time · Posted Oct 19, 2026
- Location
- Arlington, VA
Bellevue, WA
Herndon, VA - Experience
- 3+ years building models for business applications; PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience.
- Education
- PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience.
- Visa sponsorship
- Candidates must be a U.S. citizen or national, U.S. permanent resident (current Green Card holder), or lawfully admitted into the U.S. as a refugee or granted asylum.
About the job
Build and productionize scientific models and privacy-preserving data methods to detect threats across Amazon Leo’s physical and digital security telemetry.
What you'll do
- Model normal activity and detect anomalous behavior across heterogeneous security telemetry, including industrial-control data.
- Design privacy-preserving data representations and verify that models retain detection accuracy on transformed data.
- Develop detection evaluation methods and metrics, contribute to a neurosymbolic reasoning platform, and document findings for scientific and security audiences.
What they ask for
- 3+ years of building models for business application experience.
- PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience.
- Experience with statistical modeling and analysis of large, heterogeneous datasets, including anomaly detection, time-series analysis, or behavioral baselining.
- Fluency in Python; experience with popular deep learning frameworks (e.g., PyTorch, TensorFlow).
Nice to have
- Experience in security domains, professional software and systems development, or publications at peer-reviewed venues.
- Experience with privacy-preserving machine learning, evaluation frameworks, or operational and sensor telemetry.
Pay
142,800.00 - 193,200.00 USD annually (base salary); 142,800.00 - 193,200.00 USD annually (base salary); 142,800.00 - 193,200.00 USD annually (base salary)
Tools
Python, Rust, C++, PyTorch, TensorFlow
AI focus
Applies statistical and behavioral modeling, privacy-preserving machine learning, and neurosymbolic reasoning to security detection; the team is moving toward correlation-based detection, automated response, and agentic AI.
Model-written summary · the original posting is authoritative