FewerJobs.
All jobs

Sr. Applied Science Manager, Perfect Order Experience (POE) AI

Amazon - Seattle, Washington, USA

Posted Sep 17, 2025

Benefits

Parental leave
6 weeks From the posting source checked Jun 20, 2026
Non-birth-parent leave
6 weeks From the posting source checked Jun 20, 2026
Family-building benefits
  • Fertility benefits: Offered From the posting source checked Jun 20, 2026
  • Adoption assistance: Offered From the posting source checked Jun 20, 2026
  • Surrogacy assistance: Offered From the posting source checked Jun 20, 2026
Mental health support
Offered From the posting source checked Jun 20, 2026
Relocation assistance
Not verified
Childcare support
Offered From the posting source checked Jun 20, 2026
Learning budget
Not verified
Verification
Source-linked checked Jun 7, 2026
Salary
$219K-$296K From the posting source checked Jun 20, 2026
401(k) match
Reported from DOL Form 5500 industry filing (not employer-specific)

Was this benefit information wrong? Tell us.

Market context

U.S. role benchmark (BLS OEWS)
$61,842 U.S. median for this role
Projected growth (BLS Employment Projections)
+1.9% - Slower

316% above the BLS role benchmark for operations aggregate.

Posted salary is far from this role benchmark; treat it as low confidence.

Matched to SOC 11-1021 - Operations aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Role

Role function
Operations From the posting source checked Jun 20, 2026
Seniority
Senior From the posting source checked Jun 20, 2026

Schedule

Shift type
Not verified
Weekend work
Not verified

Company

Equity
Offered Verified - SEC 10-K source checked Jun 20, 2026

Application

Cover letter
Not verified
Assessment
Not verified
Deadline
Not stated

Where they hire

State eligibility is not yet verified.

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.

Read the full description at www.amazon.jobs. FewerJobs shows a preview and links to the original posting.

Apply at amazon.jobs

Apply link not verified; last-live date unavailable.

What verified means

Verified means a displayed claim has field-level provenance to a source FewerJobs pulled: a government or employer source, or the original job posting. Posting-sourced facts are employer-stated and are labeled separately from government records.

Related jobs