The Alexa team in Santa Barbara is hiring an outstanding scientist who is passionate about applying advanced ML and statistical techniques to solve real-world challenges. This role will focus on advancements in knowledge management tooling, automation of large scale data ingestion, advanced data transformation, knowledge graph structure, and natural language processing (NLP).
As part of the Alexa Information team, our group combines natural language understanding, natural language generation, large volumes of structured knowledge, and autonomous machine reasoning to answer Alexa customer questions in the most natural way possible. We've solved many complex problems to get to where we are today, but there are still plenty of challenges ahead of us. Our goal is to be able to answer every Alexa customer question, every time. We need your help to build the advancements required to make that a reality.
You will work in an agile team of scientists and engineers at our development center in Santa Barbara, CA. As a scientist on the team, you will be involved in every aspect of the development lifecycle, from idea generation and scientific research to development and deployment of advanced models.
Our ideal candidate is an experienced ML technology scientist who has a track-record of performing analysis and applying statistical techniques to solve real business problems. They will have great leadership and communication skills, and are motivated to achieve results in a fast-paced environment. The position offers an exceptional opportunity to grow your technical and non-technical skills and make a real difference to Alexa customers.
Participate in the design, development, evaluation, and deployment of data-driven models and analytical solutions for machine learning (ML) and natural language (NL) applications.
Implement and improve modeling tools, training recipes, and prototypes utilizing programming skills in Python, Java or C++.
Collaborate with software engineering teams to integrate successful experimental results into complex Amazon production systems.
Work backwards from customer needs and use that information to make trade-offs between competing modeling approaches.
Report results to technical and business audiences in a manner that is statistically rigorous and compellingly relevant, exemplifying good scientific practice in a business environment.
Research the latest modeling techniques and stay current with advancements in the field.
Drive best practices, helping to set high scientific and engineering standards on the team.
Promote the culture of experimentation and applied science at Amazon.
Mentor junior software engineers, interns, and scientists.