Lead and grow a team of software engineers building internet-scale catalog, discovery, and commerce systems for Off-Amazon Selection (OAS). You will own the technical vision, roadmap, and operational excellence for distributed systems that make the world's selection discoverable and shoppable. Partnering with Applied Scientists, you'll apply Large Language Models and generative AI to build intelligent primitives that transform how customers discover, evaluate, and purchase products across Amazon surfaces. You'll recruit and develop engineers, drive architecture in ambiguous problem spaces, and deliver robust, scalable systems at Amazon scale while earning trust with product, science, and partner engineering teams.
Key job responsibilities
In this role you will build, lead, and grow a high-performing team of software engineers, setting a high bar for quality and for the engineers you hire, mentor, and develop at every level. You will own the technical vision, roadmap, and architecture for distributed catalog, discovery, and commerce systems at internet scale, pioneering AI-native software development and partnering closely with Applied Scientists to build intelligent primitives that change how customers discover, evaluate, and purchase products. You will own the full software development life cycle, from design through deployment and operational excellence, driving services at Amazon scale with instrumented health signals so issues are caught early and decisions are data driven. Most of all, you will thrive where problems are not yet well defined, turning ambiguous, cross team challenges into clear plans with owners and dates, and earning the trust of product, science, and partner engineering teams along the way. It is a rare chance to shape both the systems and the team that make the world's selection discoverable and shoppable for customers everywhere.
A day in the life
As SDM for Off-Amazon Selection, no two days look the same. You might unblock an engineer on a catalog ingestion design, then join a working session with Applied Scientists on how an LLM-powered capabilities handles product matching at scale. You will review design docs, weigh in on architectural trade-offs, and sequence a multi-quarter roadmap against ambiguous requirements. You will invest in your people through 1:1s, career conversations, hiring, and coaching.
About the team
Off-Amazon Selection builds the next generation of catalog, discovery, and commerce systems that make the world's selection discoverable and shoppable for customers. We work at the intersection of distributed systems and applied science, using machine learning and generative AI to solve problems at scale. We think in terms of durable primitives and mechanisms rather than one-off features, building for scale, reliability, and reuse. We are a collaborative, high-ownership team that values camaraderie, diverse perspectives, and a strong engineering culture, and we tackle ambiguous, challenging problems together.
