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Amazon

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

Amazon is hiring a Sr. Applied Science Manager, Perfect Order Experience (POE) AI in Seattle, United States. It pays $219k-$296k a year and Level rates it ; you can apply on Amazon.

Level's read on this job

  • On Level's scale, this role is Builds AI ●●●●: the job is building AI systems.
  • Tools and skills the posting cites include fine tuning.
  • Level lists 20,000+ other open roles at Amazon.

AI in this role

fine-tuning
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.
- Collaborate with other science teams to develop/ influence ranker models that optimize product visibility.


About the team
About the Perfect Order Experience (POE) AI Team

The POE AI Science team sits at the forefront of Amazon's efforts to ensure customers can shop with confidence. Our team combines artificial intelligence, machine learning, and economic insights to protect product authenticity, maintain quality standards, and safeguard intellectual property across Amazon's vast catalog. The work we do directly impacts billions of customer interactions and enables millions of sellers to thrive while maintaining the highest standards of trust and quality.


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

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.

The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.



USA, WA, Seattle - 218,800.00 - 295,900.00 USD annually

How we rate this

Sr. Applied Science Manager, Perfect Order Experience (POE) AI at Amazon rates 95 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

Fine-tuning

Questions you could be asked

  1. Walk me through fine-tuning a model: what data did you use, and how did you check the result?
  2. How would you decide a model or AI system is ready to ship?
  3. Tell me about a time a model underperformed in production. How did you find out, and what did you change?

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  • List these exact terms on your resume: Fine-tuning. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
  • Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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