Are you interested in delighting Alexa customers around the world? Come join us and help shape Alexa's future. We have a position in the Alexa AI Knowledge group in Cambridge to solve challenging problems in the knowledge extraction and acquisition space.
Our challenge is to ensure Alexa understands and answers every question on any topic at any time, from popular topics such as "How old is Dresden Cathedral?", " "How long till Diwali?" to those where intelligent and common-sense reasoning are needed, such as “If Vettel wins Monaco, can Lewis still win the championship?”, “Who won the football match?”. In the Automated Knowledge Extraction team, we aim to answer these questions by retrieving relevant and up-to-date knowledge to teach Alexa about what is happening in the world every day.
We are looking for a Machine Learning Scientist to research, build and test novel techniques (for example, Deep Neural Network architectures) to acquire knowledge across a range of diverse, structured data sources, at web scale. This requires methods that lie beyond the cutting edge academic and industrial research of today. The ideal candidate is a creative problem solver, and will have industry and academic experience in Natural Language Processing (NLP) and Machine Learning (ML).
This job will test your skills across the board. You will research the latest knowledge extraction techniques and understand trade-offs between competing approaches to identify the ones that are likely to have real impact on our customers. You will help us continually re-think the problem and improve the state-of-the-art, using data-driven arguments in writing to inspire and influence a range of audiences. You will leverage your programming skills to implement and improve training recipes, model building and production prototypes. In return, you will have the opportunity to access large data sets across Amazon, guide and mentor junior Scientists, and learn from some of the best senior Scientist in the field. You will also work alongside talented Software Development Engineers to deploy advancements in the state-of-the-art in production, and be challenged by the engineering discipline needed to enable Alexa to operate at a global scale and impact millions of customers.
And while the challenges are big and the pace fast, we have fun while we’re at it. It’s a high-energy, good-natured, and inclusive environment. If you think you have what it takes to change the world, then apply to join us. We work hard, have fun, and make history at Amazon!
We believe passionately that employing a diverse workforce is central to our success and we make recruiting decisions based on your experience and skills. We welcome applications from all members of society irrespective of age, gender, disability, sexual orientation, race, religion or belief.
Amazon is an equal opportunities employer. We believe passionately that employing a diverse workforce is central to our success. We make recruiting decisions based on your experience and skills. We value your passion to discover, invent, simplify and build. Protecting your privacy and the security of your data is a longstanding top priority for Amazon. Please consult our Privacy Notice to know more about how we collect, use and transfer the personal data of our candidates.
· PhD Degree in Computer Science, Machine Learning, Computational Linguistics, Natural Language Processing, Applied Mathematics or a related field
· Hands-on experience in one or more of: Information Extraction, Deep Learning for NLP, Scalable Machine Learning, Semantic Parsing, Natural Language Generation
· Strong academic record of publications in top-tier conferences or journals
· Solid programming skills
· Experience in building ML and NLP models in Python
· Excellent communication skills and the ability to work in a multi-disciplinary, diverse team
· 2+ years post-PhD relevant industrial research experience
· Ability to convey mathematical concepts and considerations to non-experts
· Experience of working with large datasets and scaling ML models, and/or information extraction from structured sources
· Experience using Unix/Linux