Applied Scientist: ML for Denied Party Screening & Security

VulnU; Seattle (WA); On-site; USD 143,000 - 193,000; Full time

Benefits offered by this job

Paid parental leave

Retirement match

Equity

Flexible PTO

Job summary

Amazon is seeking an Applied Scientist for Denied Party Screening (DPS) within Compliance & Security Assurance in Seattle. You will lead research, design, and deployment of ML/AI models to detect and prevent prohibited transactions and support scalable, reliable screening processes.

You will collaborate with engineers, data scientists, and product teams to translate business requirements into robust systems, while staying current with state-of-the-art AI techniques and ensuring high security

Qualifications

• 3+ years building models for business applications.
• Experience programming in Java, C++, Python.
• Experience with generative deep learning models (CNNs, GANs, VAEs) and model pruning.

Responsibilities

• Drive the research, design, and development of novel ML/AI models and systems powering products and services.
• Collaborate cross-functionally to understand business requirements, customer needs, and constraints.
• Rapidly prototype, test, and iterate on ML/AI solutions based on data and feedback.
• Communicate complex technical concepts to technical and non-technical stakeholders.
• Stay up-to-date on AI advancements and apply emerging techniques.

Skills

ML modeling

Algorithms

Java

C++

Python

Generative DL

CNNs

GANs

VAEs

Model pruning

Job description

Amazon is seeking an Applied Scientist for Denied Party Screening (DPS) within Compliance & Security Assurance in Seattle. You will lead research, design, and deployment of ML/AI models to detect and prevent prohibited transactions and support scalable, reliable screening processes.

You will collaborate with engineers, data scientists, and product teams to translate business requirements into robust systems, while staying current with state-of-the-art AI techniques and ensuring high security