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Applied Scientist, Selling Partner Support Engagement

Amazon

  • Seattle, WA
  • 30+ days ago
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    Skills

    • Algorithmsunmatched
    • Application Programming Interface (API)unmatched
    • Artificial Intelligence (AI)unmatched
    • Artificial Intelligence (AI) Agentsunmatched
    • Automationunmatched
    • C++ Programming Languageunmatched
    • Channel Supportunmatched
    • Computer Scienceunmatched
    • Computer Visionunmatched
    • Conferencesunmatched
    • Continuous Improvementunmatched
    • Data Scienceunmatched
    • Data Setsunmatched
    • Data Warehousingunmatched
    • Deep Learningunmatched
    • Governmentunmatched
    • Information Retrievalunmatched
    • Information Technology & Information Systemsunmatched
    • International Salesunmatched
    • Javaunmatched
    • Large-Scale Systemsunmatched
    • Machine Learningunmatched
    • Machine Translationunmatched
    • Mathematicsunmatched
    • Mentoringunmatched
    • Metricsunmatched
    • Natural Language Processing (NLP)unmatched
    • Operations Researchunmatched
    • Oracleunmatched
    • Problem Solving Skillsunmatched
    • Product Engineeringunmatched
    • Product Lifecycleunmatched
    • Production Systemsunmatched
    • Prototypingunmatched
    • Publicationsunmatched
    • Python Programming/Scripting Languageunmatched
    • Quality Metricsunmatched
    • Rapid Prototypingunmatched
    • Relational Databases (RDBMS)unmatched
    • Research & Development (R&D)unmatched
    • SQL (Structured Query Language)unmatched
    • Salesunmatched
    • Sales Supportunmatched
    • Speech Recognitionunmatched
    • Standards Developmentunmatched
    • Statisticsunmatched
    • Voice Productsunmatched

    Description

    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 an Applied Scientist who wants to work at the intersection of 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, and we actively support it. A day in the life You read the latest research papers and implement novel techniques by building rapid prototypes using AI-assisted coding tools, then taking what works from prototype to production. You collaborate closely product managers and engineering teams to translate seller pain points into deployed science solutions. You work closely under mentorship of senior scientists to accelerate delivery of agentic solutions from development to evaluation. Finally, you attend meetings with other Amazonians to stay connected to the seller experience by understanding the real problems sellers face so your models solve what actually matters. About the team Titans Science is a growing team of scientists building the AI that powers Amazon's seller support experience. We operate in across capabilities such as Agentic Systems, Knowledge Retrieval & Query Understanding, and Content Intelligence & Automation, each owning distinct problem spaces but sharing evaluation infrastructure and research insights. We work backwards from business problems, deeply understanding the problem space and domain, defining gold-standard datasets, success metrics, and guardrails. This lets us run parallel experiments, compare approaches rigorously, and ship the best Science models to production. We publish at internal conferences and external venues, and we actively invest in research that compounds over multiple product cycles. The team sits in Seattle/Vancouver and operates with high autonomy. Scientists own their domains end-to-end, from problem framing through production deployment. We value speed over perfection, scientific rigor over polish, and experimentation over debate. We value diverse experiences. Even if you do not meet all of the preferred qualifications listed above, we encourage you to apply. The team fosters an inclusive learning culture where individual growth is a priority - you will find mentorship, knowledge-sharing, and career-advancing resources here. Basic Qualifications - Master's degree in computer science, mathematics, statistics, machine learning or equivalent quantitative field - Experience programming in Java, C++, Python or related language - Experience with SQL and an RDBMS (e.g., Oracle) or Data Warehouse Preferred Qualifications - Experience implementing algorithms using both toolkits and self-developed code - Have publications at top-tier peer-reviewed conferences or journals - PhD in Engineering, Computer Science, Machine Learning, Operations Research, Statistics, or related fields - Experience in building speech recognition, machine translation and natural language processing systems (e.g., commercial speech products or government speech projects) - Experience researching about machine learning, deep learning, NLP, computer vision, data science - Demonstrated experience leveraging generative AI tools to enhance workflow efficiency and productivity, with the ability to craft effective prompts and critically evaluate AI-generated outputs in a professional setting 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 - 136,000.00 - 184,000.00 USD annually

    Numbers & Facts

    LocationSeattle, WA
    IndustryOther/Not Classified
    Company Size10,000 employees or more
    Year Founded1994
    Websitehttp://Amazon.com/militaryroles

    About Company

    At Amazon, we don’t wait for the next big idea to present itself. We envision the shape of impossible things and then we boldly make them reality. So far, this mindset has helped us achieve some incredible things. Let’s build new systems, challenge the status quo, and design the world we want to live in. We believe the work you do here will be the best work of your life.

    Wherever you are in your career exploration, Amazon likely has an opportunity for you. Our research scientists and engineers shape the future of natural language understanding with Alexa. Fulfillment center associates around the globe send customer orders from our warehouses to doorsteps. Product managers set feature requirements, strategy, and marketing messages for brand new customer experiences. And as we grow, we’ll add jobs that haven’t been invented yet.

    It’s Always Day 1
    At Amazon, it’s always “Day 1.” Now, what does this mean and why does it matter? It means that our approach remains the same as it was on Amazon’s very first day – to make smart, fast decisions, stay nimble, invent, and stay focused on delighting our customers. In our 2016 shareholder letter, Amazon CEO Jeff Bezos shared his thoughts on how to keep up a Day 1 company mindset. “Staying in Day 1 requires you to experiment patiently, accept failures, plant seeds, protect saplings, and double down when you see customer delight,” he wrote. “A customer-obsessed culture best creates the conditions where all of that can happen.” You can read the full letter here

    Our Leadership Principles
    Our Leadership Principles help us keep a Day 1 mentality. They aren’t just a pretty inspirational wall hanging. Amazonians use them, every day, whether they’re discussing ideas for new projects, deciding on the best solution for a customer’s problem, or interviewing candidates. To read through our Leadership Principles from Customer Obsession to Bias for Action, visit https://www.amazon.jobs/principles

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