Senior ML Engineer: Personalization & Discovery

Prime Video & Amazon MGM Studios

Seattle (WA)

Presencial

USD 168 000 - 227 000

Tempo integral

Há 7 dias
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Resumo da oferta

Prime Video Personalization and Discovery (PVPD) is seeking a Senior Machine Learning Engineer to lead Personalization and Discovery customer experiences. This role will work across team boundaries within PVPD and with external partners to drive the success of AI-powered content discovery initiatives.

You'll be responsible for leading the technical strategy, architecture, and implementation of PV groupings, re-engagement and optimizations.

Qualificações

  • 5+ years of non-internship professional software development experience.
  • 5+ years of programming with at least one software programming language experience.
  • 5+ years of leading design or architecture of new and existing systems.
  • Experience as a mentor, tech lead or leading an engineering team.

Responsabilidades

  • Design, develop and maintain AI based recommendation systems services that drive personalized experiences across Prime Video.
  • Build distributed systems to manage high-volume, low-latency AI powered customer experiences.
  • Develop tooling and automation to improve the efficiency, visibility, and reliability of recommendation systems.
  • Collaborate cross-functionally with applied scientists, product management, and other software development teams to continuously enhance the Prime Video discovery and personalization experiences.

Conhecimentos

Software development
Programming
Design leadership
Mentoring

Formação académica

Bachelor's degree

Descrição da oferta de emprego

Prime Video Personalization and Discovery (PVPD) is seeking a Senior Machine Learning Engineer to lead Personalization and Discovery customer experiences. This role will work across team boundaries within PVPD and with external partners to drive the success of AI-powered content discovery initiatives.

You'll be responsible for leading the technical strategy, architecture, and implementation of PV groupings, re-engagement and optimizations.

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