Sr. Applied Science Manager, Perfect Order Experience (POE) AI
Amazon - Seattle, Washington, USA
Posted Sep 17, 2025
Benefits
- Parental leave
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- Non-birth-parent leave
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- Family-building benefits
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- Mental health support
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- Salary
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- 401(k) match
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Schedule
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- Weekend work
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Application
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Where they hire
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About this role
Sr. Applied Science Manager, Perfect Order Experience (POE) AI Seattle, Washington, USA The Perfect Order Experience (POE) AI team combines artificial intelligence, machine learning, and economic insights to ensure exceptional customer experiences and seller success on Amazon. We develop advanced scientific solutions that protect product authenticity, maintain quality standards, and safeguard intellectual property across Amazon's vast catalog. Our work spans from building detection systems using state-of-the-art Large Language Models to creating automated investigation processes and risk treatment mechanisms. Our solutions directly impact billions of customer interactions and enable millions of sellers to thrive while maintaining the highest standards of trust and quality. We are seeking an exceptional Senior Applied Science Manager to lead key AI initiatives to ensure a perfect order experience for Amazon customers. In this role, you will spearhead the development of a domain specific large language model designed to comprehend complex seller behaviors and relationships. You will lead the research and implementation on LLM pre-training, fine-tuning and reinforcement learning for LLM reasoning. You will implement and influence ranker models that intelligently adjust product visibility based on risk signals and trust metrics. Key job responsibilities - Drive AI strategy and lead a team of applied scientists in developing ML solutions. - Lead the end-to-end development of a domain specific LLM. - Drive the development of large-scale pre-training and post-training strategies for the LLM using domain-specific datasets. - Architect automated risk detection and treatment systems that combine multi-modal signals to identify product quality issues and implement optimization-based mitigation strategies.
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