Senior Applied Scientist — ML for App Security Risk

Amazon; San Francisco, Northern (CA, KY); Hybrid; USD 184,000 - 249,000; Full time

Benefits offered by this job

Health insurance

RSUs

401(k) matching

Paid time off

Parental leave

Job summary

Amazon's AppStar DNA team seeks an Applied Scientist III to invent and deploy ML-driven systems that prioritize security risks at scale across thousands of applications.

You will build production ML pipelines on AWS, develop graph-based models of relationships between apps, services, and vulnerabilities, and collaborate with security engineers to translate model outputs into actionable security strategies.

Qualifications

• 3+ years of building machine learning models for business applications.
• 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 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.

Responsibilities

• Lead the design, development, and deployment of ML models and scientific solutions for application security prioritization, complexity scoring, and risk assessment
• Frame ambiguous security problems into well-defined scientific challenges, proposing novel approaches when existing methodologies are insufficient
• Architect and implement production-grade ML pipelines (feature extraction, model training, scoring, deployment) on AWS services (S3, Glue, SageMaker, Neptune)
• Develop and extend graph-based models that capture security-relevant relationships between applications, services, teams, and vulnerabilities
• Drive the team's scientific agenda by proposing new research initiatives, conducting experiments, and iterating on models using rigorous evaluation methodologies
• Partner with security engineers, data engineers, and TPMs to translate model outputs into actionable intelligence for security review programs
• Establish and raise the bar for scientific rigor: peer review code and designs, set best practices for experimentation, and document findings for reproducibility
• Publish results internally and externally at peer-reviewed venues when appropriate

Skills

Java

C++

Python

Deep learning

Education

PhD

Master's degree + 6+ years research

Tools

TensorFlow

scikit-learn

Spark MLlib

SageMaker

Neptune

Job description

Amazon's AppStar DNA team seeks an Applied Scientist III to invent and deploy ML-driven systems that prioritize security risks at scale across thousands of applications.

You will build production ML pipelines on AWS, develop graph-based models of relationships between apps, services, and vulnerabilities, and collaborate with security engineers to translate model outputs into actionable security strategies.