12/13/2023 0 Comments Fireside design solutionsWrite technical reports for scientific research and publish results in top conference. As a member of the Query Understanding Modeling team, you will: -Define, design and implement key initiatives in the Query Understanding area such as semantic parsing framework, query classification, query rewriting, knowledge graph -Work closely with other applied scientists, engineers, engineering and product managers to understand the current state of the system and drive new enhancements to the system. We value academic collaborations and encourage our scientists and engineers to participate and publish in top conferences such as NIPS, ICML, KDD, SIGIR and The Query Understanding team is responsible for developing and deploying state of the art machine learning and NLP models to extract semantic information on product search queries issued by millions of Amazon customers each day. Our team is proud of its collaborative and open research environment, where long term thinking and risk taking are highly rewarded. You will be working alongside world-class scientists and engineers to build next generation search systems and will be able to deploy your ML models into production. Internship opportunities are also available throughout the year and we are flexible with duration and start dates. We are looking to hire Software Development Engineers (SDEs) and ML Applied Scientists at all levels, with experience in Search, Personalization, NLP, Systems, ML, DL and UI Design. We have hired ML experts from leading research labs and academia to spearhead this effort. Can we use deep learning to transfer behavioral signals from frequently purchased products in the head to products in the tail where behavioral signals are sparse? The challenge here is the scale, and the fact that the head and torso contain only a small fraction of products while the tail contains an overwhelmingly large fraction of the products in the catalog. Can we deeply understand the catalog to surface products that offer the most value to a customer? The challenge here is that the definition of value is subjective and personal, and therefore requires a deeper understanding of the customers intent as well as preferences. Some exciting questions that we expect to answer over the next few years include: Can we deeply understand customer intent and personalize their search experience even when they type broad queries such as “dress” or “espresso machine”? Can we reduce the cost of serving customer queries on Amazon by orders of magnitude using ML to predict n-grams and tuples that many queries decompose into, apply expensive ranking functions offline to identify the most relevant products that match these terms, and index these for efficient online retrieval? We expect this to lead to exciting research at the intersection of systems and ML. This is a rare opportunity to develop cutting edge ML solutions and apply them to a search problem of this magnitude. We’re looking at every aspect of search, from query understanding to front-end UX, ranking and relevance, indexing and tiering and asking how we can make big step improvements by applying advanced Machine Learning (ML) and Deep Learning (DL) techniques. We are working on a new initiative to transform our search engine into a shopping engine that assists customers with their shopping missions. Our product search engine, one of the most heavily used services in the world, indexes billions of products and serves hundreds of millions of customers world-wide. Amazon is the 4th most popular site in the US.
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