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 are breaking boundaries by allowing users to access information and services today through voice, with many more exciting projects in the pipeline.
In the Knowledge Extraction and Understanding group, we are constantly making Alexa smarter by enabling her to learn about what’s going on in the world. We use multiple Machine Learning and NLP techniques to enable learning and reasoning across a range of structured and unstructured data. We are constantly at the forefront of both research and engineering in understanding user demands and data sources, to extract the right knowledge, expand the range of, and how, Alexa accesses information – all to improve Alexa and give users the best experience. The scope of the teams in the Knowledge Extraction and Understanding group is broad, covering a diverse range of problem spaces that include, but are not limited to semantic understanding, structured data extraction, fact extraction and verification from unstructured text, natural language generation, machine translation, model adaptation, large-scale categorisation, and ranking. What’s more, we do it at scale, to bring all those solutions to millions of customers that use Alexa every day.
As an applied scientist in Knowledge Extraction and Understanding, you will bring academic and/or industrial practical experience of solving hard problems through developing state of the art research methods. You will have the opportunity to guide junior scientists, and others interested in research. You’ll also have the opportunity to learn from some of the best researchers in the field, and be challenged by the engineering discipline needed to enable Alexa to operate at a global scale.
· PhD Degree in Computer Science, Machine Learning, Computational Linguistics, Natural Language Processing, Applied Mathematics or a related field
· Post PhD experience
· 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, Natural Language Generation
· Text mining algorithms
· Semantic Attention-based Neural Network Models
· Active member of the research community
· Experience of building predictive and optimization models with knowledge of commonly used ML languages (MATLAB, Python, R).
· Excellent communication skills and the ability to working in a team
· Experience in mentoring junior scientists
· Track record of leading projects and/or building research agendas
· Ability to convey rigorous mathematical concepts and considerations to non-experts
· Relevant industrial research experience is a plus
· Experience of working with large datasets