Senior Applied Scientist, FBA AI Science & Analytics

AI in this role

Design and build next-generation multi-agent GenAI architectures and ML systems to optimize Fulfillment by Amazon.

pythonpytorchllmsreinforcement-learning

ai-evaluationmachine-learninggenerative-airecommendation-systemsmulti-agent-systems

FBA AI Science and Analytics accelerates the AI-native transformation of Fulfillment by Amazon by building, integrating, and scaling AI-powered data & science products and seller-facing experiences that drive operational efficiency and growth across Fulfillment by Amazon globally. We learn seller behaviors, design the policies and incentives that shape their experience, and ship science products that help third-party sellers grow topline and cut operating costs at Amazon scale. Our work sits at the intersection of machine learning, statistics, economics, operations research, and GenAI/LLMs.

We're looking for a Senior Applied Scientist who wants to put GenAI to work on a hard, high-visibility problem: building next-generation multi-agent systems that interact with millions of sellers and guide them through their toughest challenges at scale. You'll own solutions spanning supervised and unsupervised learning, recommendation systems, statistical learning, LLMs, harness engineering, and reinforcement learning.

The ambition is to make AI a native layer in every seller decision rather than a separate tool sellers must adopt, delivering actionable insight in minutes, not days. You'll shape end-to-end experiences across the highest-frequency seller workflows, including inventory optimization, inbound efficiency, defect improvements, reimbursements, and capacity planning. The role carries direct visibility with senior Amazon business leaders and works together with fellow scientists, engineers, and product teams to launch production-grade agentic capabilities.

Key job responsibilities
- Design, build and deploy FBA’s GenAI architectures end to end.
- Apply state-of-the-art ML and GenAI solve diverse business problems across seller supply chain systems.
- Define the team’s long-term science vision and roadmap, driven fundamentally from our customers' needs, translating those directions into specific plans for scientists, engineers, and product partners.
- Partner closely with scientists and software engineers to drive real-time model implementations and deliver high-impact features.
- Establish scalable, efficient, automated processes for large scale data analyses, model benchmarking, model evaluation and model implementation.
- Advocate the right ML solutions to business stakeholders, engineering teams, as well as executive level decision makers

Basic qualifications

- PhD, or Master's degree and 5+ years of applied research experience
- 5+ years of building machine learning models for business application experience
- Experience programming in Java, C++, Python or related language
- Knowledge of programming languages such as C/C++, Python, Java or Perl
- Expertise in deep learning, machine learning, and GenAI

Preferred qualifications

- PhD
- PhD, or a PhD or equivalent research experience and experience in patents or publications at top-tier peer-reviewed conferences or journals
- Extensive hands-on experience building and deploying agentic AI architectures and multi-agent systems

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, Bellevue - 167,100.00 - 226,100.00 USD annually

How we rate this

Senior Applied Scientist, FBA AI Science & Analytics at Amazon rates 90 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.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

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

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

AI EvaluationMachine LearningGenerative AIRecommendation SystemsMulti Agent SystemsPythonPyTorchLLMs

Questions you could be asked

How do you decide that one model's output is better than another's for a given task?
Tell me about a project where machine learning was part of your work. What did you do?
Tell me about a project where generative ai was part of your work. What did you do?
Tell me about a project where recommendation systems was part of your work. What did you do?
Tell me about a project where multi agent systems was part of your work. What did you do?

Adapt your resume

List these exact terms on your resume: AI Evaluation, Machine Learning, Generative AI, Recommendation Systems, and Multi Agent Systems. 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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