Our focus in the Alexa Knowledge team combines natural language understanding, acquiring large volumes of structured knowledge, and building autonomous machine reasoning to allow our customers to get answers to their questions in the most natural way possible. We’re part of a huge research and engineering effort on the Amazon Alexa team.
We’ve solved many complex problems to get to where we are today, but there are still plenty of challenges ahead of us, and Alexa is getting smarter every day. The problems we solve in the Alexa Knowledge team in Cambridge help Alexa get smarter by understanding the different ways people talk, by learning more and more facts about the world, by improving her common sense reasoning and by responding in the most natural way possible in multiple languages.
We set out to build Alexa at Amazon because we believe that voice will fundamentally improve the way people will interact with technology, and we wanted to create a computer in the cloud that could be controlled entirely by your voice.
On the Knowledge Extraction team, we are constantly making Alexa smarter by enabling her to learn about what’s going on in the world. We believe that the information to answer (almost) every question can be found somewhere on the internet, and not just in encyclopedia. Our goal is to teach Alexa how to read a wider range of texts and learn about the world as she reads them.
This will require methods that lie beyond the cutting edge academic and industrial research of today, at internet-level scale. Your role is to design these methods and work with the engineers on the team to bring them to the millions of customers who use Alexa every day.
- Participate in the design, development, evaluation, deployment and updating of data-driven models and analytical solutions for machine learning (ML) and/or natural language (NL) applications.
- Develop and/or apply statistical modeling techniques (e.g. Bayesian models and deep neural networks), optimization methods, and other ML techniques to different applications in business and engineering.
- Routinely build and deploy ML models on available data.
- Research and implement novel ML and statistical approaches to add value to the business. Mentor junior engineers and scientists.
- PhD Degree in Computer Science, Machine Learning, Computational Linguistics, Natural Language Processing, Applied Mathematics or a related field;
- Strong academic record of refereed publications in top tier conferences or journals;
- Hands-on experience in one or more of: Information Extraction, Deep Learning, Scalable Machine Learning, Semantic Parsing;
- Good coding skills. Experience in Python or Java is a plus;
- Good communication skills and the ability of working in a team.
- Ability to convey rigorous mathematical concepts and considerations to non-experts;
- Active member of the research community;
- Relevant post-doc or industrial research experience is a plus;
- Experience of working with large datasets.