Amazon
Sr. Applied Science Manager, Perfect Order Experience (POE) AI
Amazon, Seattle, Washington, us, 98127
Overview
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 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.
Collaborate with other science teams to develop/ influence ranker models that optimize product visibility.
Basic Qualifications
Ph.D. in Computer Science, Machine Learning, or related technical field, or equivalent practical experience
Experience leading and managing teams of scientists/engineers in delivering ML solutions at scale
Strong track record in developing and deploying production ML systems
Strong publication record or proven industrial innovations (e.g., patents) in ML/AI
Preferred Qualifications
Strong communication skills with ability to translate complex technical concepts to various audiences
Experience with LLM development, including pre-training, fine-tuning, and reinforcement learning
Knowledge of search, ranking, or recommendation systems
Experience with multi-modal ML systems combining text, image, and structured data
EEO and Accommodation
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status. Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner. Compensation and Benefits
Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $196,900/year in our lowest geographic market up to $340,300/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit https://www.aboutamazon.com/workplace/employee-benefits . This position will remain posted until filled. Applicants should apply via our internal or external career site.
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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 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.
Collaborate with other science teams to develop/ influence ranker models that optimize product visibility.
Basic Qualifications
Ph.D. in Computer Science, Machine Learning, or related technical field, or equivalent practical experience
Experience leading and managing teams of scientists/engineers in delivering ML solutions at scale
Strong track record in developing and deploying production ML systems
Strong publication record or proven industrial innovations (e.g., patents) in ML/AI
Preferred Qualifications
Strong communication skills with ability to translate complex technical concepts to various audiences
Experience with LLM development, including pre-training, fine-tuning, and reinforcement learning
Knowledge of search, ranking, or recommendation systems
Experience with multi-modal ML systems combining text, image, and structured data
EEO and Accommodation
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status. Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner. Compensation and Benefits
Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $196,900/year in our lowest geographic market up to $340,300/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit https://www.aboutamazon.com/workplace/employee-benefits . This position will remain posted until filled. Applicants should apply via our internal or external career site.
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