Description

Reinvent how millions of shoppers discover and evaluate brands by building GenAI-powered, agentic shopping experiences on Amazon Brand Stores—where traditional browsing and AI-driven conversation co-exist as a seamless continuum.

The Amazon Brand Store team (e.g., amazon.com/lego) within Sponsored Products and Brands is a core product offering in the Amazon Advertising portfolio. A brand's store is their dedicated place on Amazon to differentiate, grow sales, and build loyalty with millions of shoppers. Our mission is to empower brands of all sizes to tell their story in their own unique voice. We help brands create engaging shopping experiences that assist shoppers in discovering and evaluating them as part of their purchase decisions. We succeed when we are both useful to shoppers and when brands can attract and retain attention using our products.

A shopper browses, seamlessly steps into a guided conversation when they need help, and steps back out to browse the results. You will own ambiguous, high-impact technical problems end to end—from architecture through production operation—and partner closely with product, design, applied science, and other engineering teams to bring large language models and generative AI to millions of shoppers and the brands that serve them.

A case study on Brand Stores: https://advertising.amazon.com/library/case-studies/nespresso-brand-store-increases-shopper-engagement

Key job responsibilities
- Provide technical leadership for the team's GenAI initiatives—drive the long-term vision, architecture, and roadmap for the agentic, conversational-plus-browse shopping experience and the services behind it
- Design and deliver GenAI capabilities at the heart of the experience, applying large language models to help shoppers explore a brand's catalog, understand their options, and find the right products in the brand's voice
- Build the systems that keep GenAI output accurate, on-brand, concise, and safe
- Own mission-critical, highly scalable, low-latency systems that serve GenAI features to hundreds of millions of shopper visits—raising the bar on every service and feature the team builds
- Partner closely with applied scientists to productionize ML/GenAI models (prompting, fine-tuning, evaluation, inference, online experimentation) and integrate them into shopper-facing experiences
- Establish evaluation, experimentation (A/B), and quality-measurement frameworks for GenAI experiences—latency, faithfulness/grounding, relevance, and business outcomes
- Drive engineering excellence and operational rigor—design reviews, coding standards, reliability, scaling, and on-call best practices
- Tackle ambiguous, complex problems end to end: design, implement, test, deploy, and operate in production
- Mentor and grow engineers on the team; act as a force multiplier across teams and earn trust with partners across product, design, science, and senior leadership