Director, Applied Science, Alexa for Shopping (rufus)

Amazon Amazon · Big Tech · Seattle, WA · Applied Science

Director of Applied Science for Alexa for Shopping, leading the science vision and execution for a next-generation conversational AI platform. This role involves owning the end-to-end science roadmap for a multi-agent architecture powered by LLMs, SLMs, RL, and post-training optimization to create a personalized and intelligent shopping assistant. The focus is on distilling data, building specialized models through fine-tuning and RL, and architecting intelligent agent routing.

What you'd actually do

  1. Define and execute the science strategy for Alexa for Shopping conversational AI platform
  2. Lead a large, multidisciplinary organization of Applied Scientists, Research Scientists, and Machine Learning Engineers.
  3. Architect and scale multi-agent systems
  4. 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.
  5. Establish scientific best practices across experimentation, evaluation, model iteration, and production deployment for a high-traffic, latency-sensitive customer-facing system.

Skills

Required

  • MS in Computer Science, Machine Learning, Statistics, Operations Research, or related quantitative field.
  • 12+ years in applied machine learning and AI
  • 10+ years of people management experience, including experience as a leader of leaders managing multiple science and/or engineering teams.
  • Demonstrated track record of building and shipping production AI/ML systems at scale with direct, measurable customer impact.

Nice to have

  • Ph.D. in Computer Science, Machine Learning, Statistics, Operations Research, or related quantitative field.
  • Deep expertise in large language models, post-training techniques (RLHF, fine-tuning, distillation), and/or multi-agent systems.
  • Experience defining and executing science strategy for organizations operating at the intersection of research innovation and product delivery.
  • Strong publication record or demonstrated thought leadership in relevant areas (LLMs, NLP, RL, conversational AI, recommendation systems).
  • Excellent verbal and written communication skills with the ability to influence senior executives and translate complex technical concepts for business audiences.
  • Deep technical judgment combined with business acumen — ability to make tradeoffs across quality, latency, cost, and customer experience.

What the JD emphasized

  • building and shipping production AI/ML systems at scale with direct, measurable customer impact
  • multi-agent systems
  • post-training techniques (RLHF, fine-tuning, distillation)

Other signals

  • multi-agent architecture
  • large language models (LLMs)
  • reinforcement learning (RL)
  • post-training optimization
  • conversational AI platform