Are you looking for an opportunity to build an LLM based enterprise-grade, highly available, large scale, solution? Does it excite you to find patterns and build generic, composable solutions to solve complex problems? Are you looking for inventing newer and simpler ways of building solutions? If so, we are looking for you to fill a challenging position in Alexa Enterprise Products team.
Alexa Enterprise Products team is looking for a passionate, highly skilled and inventive Senior Applied Scientist, with strong machine learning background, to lead the development and implementation of state-of-the-art ML systems for Alexa Enterprise use-cases.
As a Senior Applied Scientist in the team, you will play a critical role in driving the development of conversational assistants, in particular those based on Large Language Models (LLMs), that meet enterprise standards. You will handle Amazon-scale use cases with significant impact on our customers' experiences.
Key job responsibilities
. You will analyze, understand and improve user experiences by leveraging Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in artificial intelligence.
. You will work on core LLM technologies, including developing best-in-class modeling, prompt optimization algorithms to enable Conversation AI use cases.
· Build and measure novel online & offline metrics for personal digital assistants and customer scenarios, on diverse devices and endpoints
· Create, innovate and deliver deep learning, policy-based learning, and/or machine learning based algorithms to deliver customer-impacting results
· Perform model/data analysis and monitor metrics through online A/B testing
Open to strong L5 AS candidates
- 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
- Solid Machine Learning background and familiar with SOTA machine learning techniques