Applied Scientist

Job summary
We have an exciting position for a NLP/ML scientist to join Alexa AI. Our team makes Alexa smarter by delivering an end-to-end natural language question answering (QA) technology. We build advanced QA models based on constructing a high precision large scale knowledge graph from multiple sources (e.g. facts extraction from text at Internet-scale; linking and aligning open and proprietary knowledge-bases); developing natural language understanding models; and generating natural language responses based on query results on our knowledge graph.

To achieve our ambition we need to develop methods that lie beyond the cutting edge academic and industrial research of today and as a scientist, you will bring academic and/or industrial practical experience and create novel solutions to complex problems at massive scale. We are particularly interested in problems of fact extraction, entity linking, natural language understanding, semantic parsing, natural language generation, language models, cross-lingual NLP models, and weakly supervised methods of learning (self-supervised, transfer learning, semi-supervised, curriculum learning).

As a research led team, we have been publishing and contributing to the scientific community and you can find some of our recent work at: Rongali et al, "Don't Parse, Generate! A Sequence to Sequence Architecture for Task-Oriented Semantic Parsing", https://arxiv.org/abs/2001.11458; Harkous et al, "Have Your Text and Use It Too! End-to-End Neural Data-to-Text Generation with Semantic Fidelity", https://arxiv.org/abs/2004.06577; Sen et al, "What do Models Learn from Question Answering Datasets?", https://arxiv.org/abs/2004.03490; "End-to-End Entity Resolution and Question Answering Using Differentiable Knowledge Graphs", https://aclanthology.org/2021.emnlp-main.345/; Thorne et al, FEVER: a large-scale dataset for fact extraction and verification, https://arxiv.org/abs/1803.05355.

Basic Qualifications

• PhD degree in Computer Science, Machine Learning, Computational Linguistics, Natural Language Processing, Semantic Web, Applied Mathematics or a related field;
• Hands-on experience in one or more of: Information Extraction, Knowledge Fusion, Fact Verification, Deep Learning, Scalable Machine Learning;
• Active member of the research community and strong track record of scientific publications in premier journals or conferences;
• Experience of building ML and NLP models in Python;
• Good coding skills, experience in Python or Java is a plus;
• Ability to work on ambiguous problem areas and deliver business critical solutions.
• Track record of leading projects and/or building research agendas
• Excellent communication skills (both with technical and non-technical audiences) and the ability to working in a team
• Experience in mentoring junior scientists

Preferred Qualifications

• 5+ years of post-PhD relevant academic or industrial research experience;
• Experience with delivering production AI systems;
• Experience of working with large datasets;
• Expertise in fact verification, entity resolution, and knowledge fusion;

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 (https://www.amazon.jobs/en/privacy_page) to know more about how we collect, use and transfer the personal data of our candidates.

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