Applied Scientist, ML Security & Denied-Party Screening

Amazon Inc.

Seattle (WA)

Presencial

USD 143.000 - 193.000

Jornada completa

Hace 12 días

Una candidatura completa en un minuto

Currículum y carta de presentación personalizados, listos para enviar.

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Descripción de la vacante

Amazon seeks an Applied Scientist to tackle challenging ML/AI problems within Amazon Security. You will collaborate with engineers, data scientists, and domain experts to define innovative ML/AI-powered customer experiences and solutions that scale.

You will drive research, prototyping, and evaluation, translating insights into deployable models while mentoring junior scientists. This role emphasizes curiosity, technical depth, and impact across teams in a Day 1 environment.

Formación

  • 3+ years of building models for business applications.
  • Experience building ML models or developing algorithms for business applications.
  • Experience programming in Java, C++, Python or related language.

Responsabilidades

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

Conocimientos

Java
C++
Python
Generative DL models

Descripción del empleo

Amazon seeks an Applied Scientist to tackle challenging ML/AI problems within Amazon Security. You will collaborate with engineers, data scientists, and domain experts to define innovative ML/AI-powered customer experiences and solutions that scale.

You will drive research, prototyping, and evaluation, translating insights into deployable models while mentoring junior scientists. This role emphasizes curiosity, technical depth, and impact across teams in a Day 1 environment.

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