Senior Applied Scientist, Security Issue Management

Amazon•Seattle, WA

About The Position

Are you interested in building Agentic AI solutions that solve complex builder experience challenges with significant global impact? The Security Tooling team designs and builds high-performance AI systems using LLMs and machine learning that identify builder bottlenecks, automate security workflows, and optimize the software development lifecycle—empowering engineering teams worldwide to ship secure code faster while maintaining the highest security standards. As a Senior Applied Scientist on our Security Tooling team, you will focus on building state-of-the-art ML models to enhance builder experience and productivity. You will identify builder bottlenecks and pain points across the software development lifecycle, design and apply experiments to study developer behavior, and measure the downstream impacts of security tooling on engineering velocity and code quality. Our team rewards curiosity while maintaining a laser-focus on bringing products to market that empower builders while maintaining security excellence. Competitive candidates are responsive, flexible, and able to succeed within an open, collaborative, entrepreneurial, startup-like environment. At the forefront of both academic and applied research in builder experience and security automation, you have the opportunity to work together with a diverse and talented team of scientists, engineers, and product managers and collaborate with other teams. This role offers a unique opportunity to work on projects that could fundamentally transform how builders interact with security tools and how organizations balance security requirements with developer productivity.

Requirements

5+ years of building machine learning models or developing algorithms 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 with large scale machine learning systems such as profiling and debugging and understanding of system performance and scalability
Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.

Responsibilities

Design and implement novel AI/ML solutions for complex security challenges and improve builder experience
Drive advancements in machine learning and science
Balance theoretical knowledge with practical implementation
Navigate ambiguity and create clarity in early-stage product development
Collaborate with cross-functional teams while fostering innovation in a collaborative work environment to deliver impactful solutions
Design and execute experiments to evaluate the performance of different algorithms and models, and iterate quickly to improve results
Establish best practices for ML experimentation, evaluation, development and deployment
Integrate ML models into production security tooling with engineering teams
Build and refine ML models and LLM-based agentic systems that understand builder intent
Create agentic AI solutions that reduce security friction while maintaining high security standards
Prototype LLM-powered features that automate repetitive security tasks
Design and conduct experiments (A/B tests, observational studies) to measure downstream impacts of tooling changes on engineering productivity
Present experimental results and recommendations to leadership and cross-functional teams
Gather feedback from builder communities to validate hypotheses

Benefits

Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit https://www.aboutamazon.com/workplace/employee-benefits .

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