Senior Applied Scientist, Amazon Business - External Integrations
Confirmed on the employer's own hiring board on Oct 28, 2026. First seen by Alion on Oct 28, 2026. Amazon scores B on the Alion truth index.
Amazon Business System Integrations connects Amazon Business with millions of business users and thousands of partner procurement, ERP, and finance systems. We build the APIs, protocols, and agentic experiences that let customers buy on Amazon Business directly from the systems they already use. As an Applied Scientist on this team, you will own and advance the ML and GenAI capabilities that make these integrations smarter, faster, and more reliable. You will drive model accuracy, scientific innovation, and global scale across external systems integrations.
Our work includes API-based and protocol-based shopping, such as punchout and emerging agent protocols, along with agentic interactions where buyers and AI agents search, compare, and purchase through natural language. It also covers partner onboarding and integration automation, mapping and normalizing catalog, product, and order data across many partner formats, and evaluation frameworks that measure the quality, accuracy, and latency of our agents.
Key job responsibilities
As an Applied Scientist on this team, you will work across three dimensions: Invent, Implement, and Influence.
Invent: You bring deep domain knowledge and fluency with state-of-the-art scientific approaches and emerging research. You practice customer-obsessed science, working backwards from the needs of business buyers, procurement teams, and integration partners to extend or invent new ML approaches, even when no textbook solution exists. You design novel ML and LLM-based methods for agentic shopping, product search and matching, schema and data mapping across partner systems, and automated evaluation of agent quality. You help define the research agenda for our agentic and protocol-based capabilities, and you lead the scientific strategy for accurate, low-latency responses in conversational and API-driven procurement. You author or co-author articles for internal or external peer-reviewed venues when appropriate and not precluded by business considerations.
Implement: The ML components you develop are integrated directly into production systems serving Amazon Business customers and partners worldwide. You make sound tradeoffs between accuracy and latency, innovation and stability, and short-term and long-term solutions, favoring reuse and established frameworks where appropriate. You make progress semi-autonomously with only occasional guidance, implement at the right level of complexity the first time, and evaluate emerging technologies, including LLMs and GenAI frameworks, largely on your own. You ensure your models and pipelines integrate robustly with catalog, offer, cart, order, and partner data sources and with the services that power external integrations.
Influence: You contribute to planning for the team, including goals, priorities, and roadmaps for ML and GenAI capabilities. You lead the scientific strategy for expanding agentic and integration capabilities across new partners, protocols, and countries. You collaborate with engineering, product, and program teams across Amazon Business so that data integration and ML pipelines are robust and scale with adoption. You mentor scientists and engineers, champion best practices for the AI-Driven Development Life Cycle (AIDLC), and provide peer feedback on research methods and results. You help recruit bar-raising talent through interview drives, and you are visible in the broader scientific community as a subject matter expert, including serving as a Program Committee member or reviewer for peer-reviewed venues.
Basic qualifications
- PhD, or Master's degree and 8+ years of applied research experience
- Experience programming in Java, C++, Python or related language
Preferred qualifications
- Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
- Experience with large scale distributed systems such as Hadoop, Spark etc.
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