Director, Applied Science, Alexa for Shopping (Rufus)

Alexa for Shopping (Rufus) is Amazon"s new AI-powered shopping assistant that combines the capabilities of Rufus and Alexa+ to provide a more personalized and intelligent shopping experience. We are building the future of AI-powered commerce, where every customer interaction is conversational, personalized, and proactive.

We are seeking a Director, Applied Science to lead the science vision and execution for the next-generation conversational AI platform. This leader will own the end-to-end science roadmap for a multi-agent architecture powered by large language models (LLMs), SLMs, reinforcement learning (RL), and post-training optimization to deliver the most helpful, accurate, and fastest AI shopping assistant in the industry.

This is a transformational leadership role. You will lead the science that makes this possible: distilling Amazon"s vast data assets into rich context, building specialized models through fine-tuning and RL that match frontier model quality at a fraction of the latency, and architecting intelligent agent routing across diverse use cases (pre-purchase, post-purchase, cross-Amazon services).

The ideal candidate is deeply steeped in LLM-based architectures, post-training techniques (RLHF, DPO, fine-tuning), and multi-agent systems. They are passionate about applied science, working back from customer experience to define what matters, and building teams that ship production AI at scale. This leader will shape the science philosophy for one of Amazon"s highest-visibility AI initiatives.

Key job responsibilities

• Define and execute the science strategy for Alexa for Shopping conversational AI platform
• Lead a large, multidisciplinary organization of Applied Scientists, Research Scientists, and Machine Learning Engineers.
• Architect and scale multi-agent systems
• Partner with Product, Engineering, and senior leadership (including S-team) to align AI investments with long-term business goals and the vision of conversational commerce replacing traditional shopping paradigms.
• Establish scientific best practices across experimentation, evaluation, model iteration, and production deployment for a high-traffic, latency-sensitive customer-facing system.
• Mentor and develop senior technical leaders; foster a culture of innovation, customer obsession, and operational excellence.