The Principal Applied Scientist will own the science mission for building next-generation proactive and autonomous agentic experiences across Alexa AI's Personalization, Autonomy and Proactive Intelligence organization. You will technically lead a team of applied scientists to harness state-of-the-art technologies in machine learning, natural language processing, LLM training and application, and agentic AI systems to advance the scientific frontiers of autonomous intelligence and proactive user assistance. The right candidate will be an inventor at heart, provide deep scientific leadership, establish compelling technical direction and vision, and drive ambitious research initiatives that push the boundaries of what's possible with AI agents.
You will need to be adept at identifying promising research directions in agentic AI, developing novel autonomous agent solutions, and translating advanced AI research into production-ready agentic systems. You will need to be adept at influencing and collaborating with partner teams, launching AI-powered autonomous agents into production, and building team mechanisms that will foster innovation and execution in the rapidly evolving field of agentic AI. This role represents a unique opportunity to tackle fundamental challenges in how Alexa proactively understands user needs, autonomously takes actions on behalf of users, and delivers intelligent assistance through state-of-the-art agentic AI technologies.
As a science leader in Alexa AI, you will shape the technical strategy for making Alexa a truly proactive and autonomous agent that anticipates user needs, takes intelligent actions, and provides seamless assistance without explicit prompting. Your team will be at the forefront of solving complex problems in agentic reasoning, multi-step task planning, autonomous decision-making, proactive intelligence, and context-aware action execution that will fundamentally transform how users interact with Alexa as an intelligent agent.
The successful candidate will bring deep technical expertise in machine learning, natural language processing, and agentic AI systems, along with the leadership ability to guide talented scientists in pursuing ambitious research that advances the state of the art in autonomous agents, proactive intelligence, and AI-driven personalization. Experience with multi-agent systems, reinforcement learning, goal-oriented dialogue systems, and production-scale agentic architectures is highly valued.
You will lead the development of breakthrough capabilities that enable Alexa to: 1) proactively anticipate user needs through advanced predictive modeling and contextual understanding; 2) autonomously execute complex multi-step tasks with minimal user intervention; 3) reason and plan intelligently across diverse user goals and environmental contexts; 4) learn and adapt continuously from user interactions to improve agentic behaviors; 5) coordinate actions seamlessly across multiple domains and services as a unified intelligent agent.
This is a unique opportunity to define the future of conversational AI agents and build technology that will impact hundreds of millions of customers worldwide.
Key job responsibilities
Technical Leadership
- Lead complex research and development projects
- Partner closely with the T&C Product and Engineering leaders on the technical strategy and roadmap
- Evaluate emerging technologies and methodologies
- Make high-level architectural decisions
Technical leadership and mentoring:
- Mentor and develop technical talent
- Set team project goals and metrics
- Help with resource allocation and project prioritization from technical side
Research & Development
- Drive innovation in applied science areas
- Translate research into practical business solutions
- Author technical papers and patents
- Collaborate with academic and industry partners
About the team
PAPI (Personalization Autonomy and Proactive Intelligence) aims to accelerate personalized and intuitive experiences across Amazon's customer touchpoints through automated, scalable, self-serve AI systems. We leverage customer, device, and ambient signals to deliver conversational, visual, and proactive experiences that delight customers, increase engagement, reduce defects, and enable natural interactions across Amazon touch points including Alexa, FireTV, and Mobile etc. Our systems offer personalized suggestions, comprehend customer inputs, learn from interactions, and propose appropriate actions to serve millions of customers globally.
BASIC QUALIFICATIONS
- M.S. in Computer Science, Machine Learning, or a related field.
- 10+ years experience in natural language processing, machine learning, recommendation, search, multimodal AI, or a related field.
- Proven track record of leading and managing science and engineering teams in building complex, real-time systems involving AI, ML, NLP with successful delivery to customers.
- Demonstrated track record of project delivery for large, cross-functional projects with evolving requirements. Ability to take a project from requirements gathering and design to actual product launch, customer validation and scaling.
- Exceptional customer relationship skills including the ability to discover the true requirements underlying feature requests, recommend alternative technical and business approaches, and lead combined science and engineering efforts to meet timelines with optimal solutions.
PREFERRED QUALIFICATIONS
Preferred qualifications
- Ph.D. in Computer Science, Machine Learning, or a related field.
- 10+ years experience in recommendations, search, natural language processing, machine learning, deep learning, Generative AI, Agentic AI or a related field.
- Demonstrated ability to push the envelope in at least one machine learning domain (e.g. deep learning, NLP, reinforcement learning)
- Expertise in large language models or demonstrated ability to develop this expertise quickly.
- Excellent written and verbal technical communication with an ability to present complex technical information in a clear and concise manner to a variety of audience.
- Expertise in end-consumer AI experiences, ideally with learning/ experiential offerings.
- Demonstrated track record of peer-reviewed scientific publications that advance state-of-the art for applied science.
