The AURORA: Alexa Understanding, Runtime, ORchestration, and Applied sciences org is looking for an Applied Science Manager with a background in Natural Language Processing, Machine/Deep Learning, and Large Language Models (LLMs). You will be working with a team of Applied Scientists to enhance existing features and explore new possibilities with LLM empowerment. You will own high visibility programs with broad visibility and global impact. You will interact with a cross-functional team of Science, Product, and Engineering leaders.
We are looking for an Applied Science Manager who will play a key role in the next generation of AI powered Conversational Assistants.
Core Leadership & Team Management
- Lead and manage applied scientists working on conversational AI capabilities across the full Alexa+ agent execution path
- Build and develop high-performing science teams focused on understanding, reasoning, evaluation, and runtime systems
- Foster cross-functional collaboration between science and engineering teams to deliver both customer-facing (CX) and developer-facing (DX) experiences
Platform & Innovation
- Develop modular, reusable platforms that enable 1P and 3P engineers and scientists to accelerate innovation
- Transform how builders create conversational AI solutions at scale
- Drive evaluation tooling and assessment frameworks to measure conversational experience quality
Cross-Organizational Collaboration
- Partner with teams across the organization to align goals and accelerate delivery
- Navigate organizational changes and simplify interactions between components and teams
- Establish clear communication channels and workflows across reporting lines
Strategic Impact
- Contribute to the team's mission of being the "AI runtime backbone and horizontal intelligence team" for Alexa
- Balance advancing research with delivering robust, scalable production solutions
- Drive both innovation velocity and operational excellence simultaneously
Key job responsibilities
- Lead and manage a team of Applied and Data scientists responsible for building and enhancing capabilities for Alexa+
- Collaborate with cross-functional teams to build methods to align Amazon’s LLMs with human preferences.
- Identify and prioritize research opportunities that have the potential to significantly impact our AI systems.
- Mentor and guide team members to achieve their career goals and objectives.
- Communicate research findings and progress to senior leadership and stakeholders.
- Rapidly experiment and drive productisation to deliver customer impact.
- Drive academic partnership with top tier Indian university as part of the org’s AI/ML Center initiative.
- Participate in and drive science publications in peer-reviewed venues of repute.
About the team Aurora is the AI runtime backbone and horizontal intelligence team that powers Alexa's core infrastructure, AI capabilities, and specialized conversational models. We revolutionize conversational AI through three core pillars: architecting mission-critical AI runtime systems, advancing science solutions that connect key conversational capabilities, and transforming how builders create at scale. We empower 1P and 3P engineers and scientists worldwide with modular, reusable platforms that accelerate innovation—while delivering accurate, responsive, and reliable conversational experiences to millions of end-users through operational excellence at scale.
Basic Qualifications
- Master's degree in engineering, technology, computer science, machine learning, robotics, operations research, statistics, mathematics or equivalent quantitative field
- 5+ years of scientists or machine learning engineers management experience
- Knowledge of ML, NLP, Information Retrieval and Analytics
- Experience building complex highly-scalable systems that involve predictive models or applications of machine learning
- Experience directly managing scientists or machine learning engineers
Preferred Qualifications
- Experience building machine learning models or developing algorithms for business application
- Experience building complex software systems, especially involving deep learning, machine learning and computer vision, that have been successfully delivered to customers
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