Senior Applied Scientist , Alexa AI Aurora

Key job responsibilities

• Define the science roadmap and advance core science primitives for conversation modelling, content generation, and automated quality assurance via state-of-the-art computer vision, machine learning, and generative AI
• Arcitect agentic systems, making high-judgment trade-offs across audio/text/visual quality, relevance, latency, cost, and long-term extensibility
• Establish evaluation frameworks, metrics, and success criteria for the team's scientific initiatives, institutionalizing rigorous validation across customer touch points
• Own end-to-end delivery of complex, ambiguous research initiatives from problem formulation through experimentation to production deployment, with minimal guidance
• Identify whitespace opportunities by staying at the forefront of AI/ML advances and translating them into actionable research directions with clear customer and business impact
• Drive development and deployment of scalable agentic systems for conversation understanding and generation, ensuring architectural decisions support long-term platform evolution
• Set and continuously raise the scientific and engineering bar across the team
• Tackle the team's most complex technical problems while maintaining practical focus on customer value and solution generalizability
• Advance the team's scientific reputation through high-impact publications and presentations at top-tier internal and external venues, and generate intellectual property through patents

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

• 3+ years of building machine learning models for business application experience
• PhD, or Master's degree and 6+ years of applied research experience
• Experience programming in Java, C++, Python or related language
• Experience with neural deep learning methods and machine learning

Preferred Qualifications

• Experience in building machine learning models for business application
• Experience with large scale distributed systems such as Hadoop, Spark etc.
• Experience in building speech recognition, machine translation and natural language processing systems (e.g., commercial speech products or government speech projects)
• Usage of Generative AI tools to enhance workflow efficiency, with a willingness to learn effective agentic prompting and evaluation practices