Design privacy-preserving representations of sensitive security data, and verify that models and detections tuned against them remain accurate against the real data — turning data-protection guarantees into measurable, provable properties rather than assertions.
Define the methodology and own the analysis for difficult, loosely defined problems: gather complex data across domains, select the right techniques from a range of data science methods, and justify your approach with evidence.
Develop the metrics and evaluation frameworks that measure model and detection performance against threat actor behavior before a model is trusted in production.
Contribute to the team's neurosymbolic reasoning platform, adapting state-of-the-art techniques from the literature and shipping components at production quality.
Document your work with the rigor of a peer-reviewed publication, and communicate results clearly to both scientific and security-operations audiences.
A day in the life
You will move between analysis and production in the same week: profiling a telemetry source the team has never modeled, establishing what normal looks like for it, and shipping the baseline as a component detections build on. Security engineers on your team translate threat intelligence into the adversary behaviors that matter; you build and tune the models that detect those behaviors and evaluate model performance against them. You might spend a morning chasing a false-positive pattern to its statistical root cause, and the afternoon verifying that an anonymized dataset still preserves the signal a detection depends on. You will work semi-autonomously with guidance from senior scientists, backtest candidate detections against retained telemetry, and deliver scientific artifacts that ship.
About the team
Leo Infrastructure and IP Security protects the people, facilities, hardware, and supply chain behind a global satellite constellation. The Engineering and R\&D team within this organization builds the platforms and tooling the security pillar teams operate on, moving security operations from manual triage to correlation-based detection, automated response, and agentic AI. The team is composed of applied scientists, software engineers, and security engineers working across physical and digital security domains.
#### 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 professional.
#### Work/Life Balance
We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why flexible work hours and arrangements are part of our culture. When we feel supported in the workplace and at home, there's nothing we can't achieve.
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 a systems language such as Rust or C++ is a plus
Experience with popular deep learning frameworks (e.g., PyTorch, TensorFlow)
Experience in one or more of the following domains: access- control system and methodology, network security, application- and system-development security, security architecture and models, cryptography, and operations security
Experience in professional software and systems development
Publications at top-tier peer-reviewed machine learning, data mining, or security venues (e.g., NeurIPS, ICML, ICLR, KDD, CCS, USENIX Security)
Experience with privacy-preserving machine learning, including synthetic data generation, anonymization, or differential privacy, and validating fidelity of transformed data
Experience designing evaluation frameworks and metrics for models where ground truth requires human judgment
Experience modeling operational or sensor telemetry, including industrial control systems (OT/SCADA), IoT, or physical-world event data
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.
The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.
USA, VA, Arlington - 142,800.00 - 193,200.00 USD annually
USA, VA, Herndon - 142,800.00 - 193,200.00 USD annually
USA, WA, BELLEVUE - 142,800.00 - 193,200.00 USD annually
USA, WA, Seattle - 142,800.00 - 193,200.00 USD annually
