Amazon.com Services LLC
Research Science, Applied Science, selling partner services
SeniorAppliedScientist,SellingPartnerSupportEngagement
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“Senior Applied Scientist, Selling Partner Support Engagement at Amazon.com Services LLC. Skills: Reinforcement learning, Agentic architectures, Large-scale systems, Machine Learning. Own research and development. Design novel approaches”
What You'll Achieve.
Improve millions seller interactions; Measurably improve interactions
Industry & Context.
Problem formulation; ML problems; Root cause analysis
What They're Looking For.
Must Have
7+ years applied research, 5+ years ML models business, PhD or Master's degree, Programming in Java, C++, Python, Experience neural deep learning, Experience machine learning methods
Nice to Have
Modeling tools R, scikit-learn, Modeling tools Spark MLLib, Modeling tools MxNet, Tensorflow, Modeling tools numpy, scipy, Experience design tools, Experience wire-framing tools, Experience prototyping tools, Leveraging generative AI tools, Identify AI solutions opportunities, Build scaling agentic system components
What You'll Do.
Own research and development
Design novel approaches
Build evaluation frameworks
Collaborate with scientists
Partner with engineering teams
Deploy models into production
Mentor junior scientists
Implement novel techniques
Build rapid prototypes
Translate seller pain points
Bring state of the art
Drive science roadmap
Identify new research directions
Make case for building
Mentor other scientists
Raise bar on execution
Get mentored by Principals
Stay connected to seller experience
Understand real problems
Resolve seller issues
Learn from interactions
Improve with minimal intervention
Understand seller context
Select right solution
Route contacts optimally
Automate resolution end-to-end
Augment associates with AI
Frame ambiguous challenges
How You'll Work.
Team & Collaboration
Partner with engineering; Collaborate with scientists; Partner with product managers; Work with engineering teams; Cross-functional teams
Communication Scope
Executive presentations
Process & Methodology
Roadmap planning
Full Job Description
Amazon's Selling Partner Support handles tens of millions of contacts annually worldwide. The Titans Science team is transforming this experience by building AI agents that autonomously resolve seller issues, learn from every interaction, and continuously improve with minimal human intervention. These agents reason, remember, and adapt — from understanding the seller's context and selecting the right solution, to routing contacts optimally, automating resolution end-to-end, and augmenting associates with AI when human judgment is needed. We do this in deep partnership with multiple engineering and product partners. We are looking for a Senior Applied Scientist who wants to work at the intersection of reinforcement learning, agentic architectures, and large-scale production systems. You will be directly connected to the problems sellers face every day, translating real customer pain into science solutions that operate at massive scale. You will frame ambiguous business challenges as tractable ML problems to shipping systems that measurably improve millions of seller interactions. Key job responsibilities - Own end-to-end research and development of RL-based agent improvement systems — from problem formulation through production deployment and impact measurement. - Design novel approaches to preference learning, reward modeling, and policy optimization in the context of conversational agents operating over real-world tools and APIs. - Build and maintain evaluation frameworks that measure agent quality across multiple dimensions: helpfulness, correctness, safety, and alignment with operational standards. - Collaborate with a team of scientists that work on forefront of Natural Language Understanding, Optimization, Machine Learning and Statistics - Partner with 10+ engineering teams to deploy models into production systems serving sellers worldwide. - Publish research at top venues (NeurIPS, ICML, EMNLP, AMLC) — the complexity of our problems produces publishable work,
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