Senior Applied Scientist, AWS Security

Amazon • Herndon, VA

5d • Onsite

About The Position

We build AI-powered tooling that enables security operations to scale with AWS's growth. Our portfolio includes generative AI incident response assistants, natural language-driven response, detection enrichment pipelines, and security data analytics platforms. Security analysts depend on these systems around the clock.

We are hiring a Senior Applied Scientist to own the science strategy for our AI security response platform. You will define and execute the machine learning and AI roadmap across our service portfolio, from large language model-powered incident triage to anomaly detection in security telemetry. You will extend and invent techniques at the product level, partnering with software and security engineers to bring models from research into production systems that operate 24/7/365. You will be the scientific authority on the team, expected to teach, mentor, and set the technical bar for how we apply AI to security operations problems.

This role requires deep expertise in natural language processing, generative AI, or a closely related discipline, combined with a demonstrated ability to translate scientific methods into production systems that solve real business problems. You will operate in high-ambiguity, high-consequence domains where your scientific judgment directly affects security outcomes for AWS.

Requirements

• 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

Nice To Haves

• Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
• Experience with large scale machine learning systems such as profiling and debugging and understanding of system performance and scalability
• Experience in applied research

Responsibilities

• Define and own the science strategy for the team's AI-powered security automation portfolio, including model selection, evaluation methodology, and research direction.
• Design and implement LLM-powered systems for security incident triage, including retrieval-augmented generation, prompt engineering, and fine-tuning approaches that improve recommendation accuracy and reduce analyst toil.
• Build anomaly detection and classification models across security telemetry data sources to surface threats, reduce false positives, and prioritize analyst attention.
• Partner with software engineers to move models from experimentation to production. Define system-level technical requirements, guide adaptation to meet production constraints, and own model performance in deployment.
• Develop evaluation frameworks and metrics that measure model effectiveness against security outcomes, not just standard ML benchmarks.
• Mentor software and security engineers on ML best practices and raise the science bar across the team through design reviews, code reviews, and knowledge sharing.

Benefits

• sign-on payments
• restricted stock units (RSUs)
• 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
• parental leave

What This Job Offers

• Job Type: Full-time
• Career Level: Senior