Amazon Science homepage

<!DOCTYPE html> <html class="HomePage" lang="en"> <head> <meta charset="UTF-8"> <meta name="viewport" content="width=device-width, initial-scale=1, maximum-scale=5"> <style data-cssvarsponyfill="true"> :root { --primaryColor: #007cb6; --secondaryColor: #e3661b; --errorColor: #f44336; --primaryTextColor: #232f3e; --secondaryTextColor: #6c7778; --headerBgColor: #ffffff; --headerBorderColor: #aab7b8; --headerMenuBgColor: #ffffff; --headerMenuSubNavTextColor: #232f3e; --aboveBgColor: #fafafa; --belowBgColor: #fafafa; --footerBgColor: #232f3e; --footerTextColor: #ffffff; --buttonBgColor: transparent; --buttonTextColor: #007cb6; --primaryHeadlineFont: Amazon Ember; --secondaryHeadlineFont: Amazon Ember; --bodyFont: Amazon Ember; --contentWidth: 1240px; } </style> <link data-cssvarsponyfill="true" class="Webpack-css" rel="stylesheet" href="https://assets.amazon.science/resource/0000016e-128c-d913-a16f-9edc0a5f0000/styleguide/All.min.546ea938cba70f2c3d06388869be681f.gz.css"> <style>.PromoRelatedContent a.Link { text-decoration: none; }</style> <title>Amazon Science homepage</title><meta name="description" content="Learn about Amazon's scientific research, science community, and career opportunities in artificial intelligence (AI), machine learning (ML), computer vision, robotics, quantum, economics and more."><meta name="keywords" content="AGI,AI,AWS,Academic Engagements,Alexa,Amazon,Amazon Science,Amazon Web Services,Artificial Intelligence,Careers"><link rel="canonical" href="https://www.amazon.science/"><meta name="brightspot.contentId" content="0000016e-2fb8-da81-a5ef-3ff8cb300000"> <link rel="alternate" href="https://www.amazon.science/" hreflang="x-default"> <link rel="alternate" href="https://www.amazon.science/" hreflang="en"><link rel="apple-touch-icon" sizes="180x180"href="/apple-touch-icon.png"><link rel="icon" type="image/png"href="/favicon-32x32.png"><link rel="icon" type="image/png"href="/favicon-16x16.png"> <meta property="og:title" content="Amazon Science"> <meta property="og:url" content="https://www.amazon.science/"> <meta property="og:image" content="https://assets.amazon.science/dims4/default/ce84994/2147483647/strip/true/crop/1200x630+0+0/resize/1200x630!/quality/90/?url=http%3A%2F%2Famazon-topics-brightspot.s3.amazonaws.com%2Fscience%2F32%2F80%2Fc230480c4f60a534bc077755bae7%2Famazon-science-og-image-squid.png"> <meta property="og:image:url" content="https://assets.amazon.science/dims4/default/ce84994/2147483647/strip/true/crop/1200x630+0+0/resize/1200x630!/quality/90/?url=http%3A%2F%2Famazon-topics-brightspot.s3.amazonaws.com%2Fscience%2F32%2F80%2Fc230480c4f60a534bc077755bae7%2Famazon-science-og-image-squid.png"> <meta property="og:image:width" content="1200"> <meta property="og:image:height" content="630"> <meta property="og:image:type" content="image/png"> <meta property="og:image:alt" content="Amazon Science Promo Image (Squid)"> <meta property="og:description" content="Amazon's approach to customer-obsessed science. Get the latest news about innovations in artificial intelligence and machine learning, including job opportunities, publications, conferences, events and more."> <meta property="og:site_name" content="Amazon Science"> <meta property="og:type" content="website"> <meta name="twitter:card" content="summary_large_image"/> <meta name="twitter:description" content="Amazon's approach to customer-obsessed science. Get the latest news about innovations in artificial intelligence and machine learning, including job opportunities, publications, conferences, events and more."/> <meta name="twitter:image" content="https://assets.amazon.science/dims4/default/2e80245/2147483647/strip/true/crop/1120x630+40+0/resize/1200x675!/quality/90/?url=http%3A%2F%2Famazon-topics-brightspot.s3.amazonaws.com%2Fscience%2F32%2F80%2Fc230480c4f60a534bc077755bae7%2Famazon-science-og-image-squid.png"/> <meta name="twitter:image:alt" content="Amazon Science Promo Image (Squid)"/> <meta name="twitter:site" content="@AmazonScience"/> <meta name="twitter:title" content="Amazon Science"/> <meta property="fb:app_id" content="1024652704536162"> <link type="application/rss+xml" rel="alternate" title="Amazon Science homepage" href="https://www.amazon.science/index.rss"> <meta name="brightspot.cached" content="false">  <script> /** @license @nocompile Copyright (c) 2018 The Polymer Project Authors. All rights reserved. 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Insights from Ray Solomonoff’s theory of induction and stochastic realization theory may help us envision — and guide — the limits of scaling.</div> <div class="PromoA-category"> <a class="Link" href="https://www.amazon.science/research-areas/machine-learning" data-cms-ai="0" >Machine learning</a> </div> </div> </div> </li> <li class="ListA-items-item"> <div class="PromoA" data-content-type="blog post" data-image-align="top" > <div class="PromoA-media"> <a class="Link" aria-label="A quick guide to Amazon’s 50-plus papers at EMNLP 2024" href="https://www.amazon.science/blog/a-quick-guide-to-amazons-50-plus-papers-at-emnlp-2024" data-cms-ai="0" ><picture><source type="image/webp" width="535" height="300" data-image-size="promoMedium" 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alt="DeCRIM.png" width="535" height="300" data-src="https://assets.amazon.science/dims4/default/ac7b984/2147483647/strip/true/crop/1733x972+3+0/resize/535x300!/quality/90/?url=http%3A%2F%2Famazon-topics-brightspot.s3.amazonaws.com%2Fscience%2Ff9%2F7c%2F62b16926491ebd48a62a8c1903e7%2Fdecrim.png" data-lazy-load="true" src="data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZlcnNpb249IjEuMSIgaGVpZ2h0PSIzMDBweCIgd2lkdGg9IjUzNXB4Ij48L3N2Zz4="> </picture> </a> </div> <div class="PromoA-content"> <div class="PromoA-title"> <a class="Link" href="https://www.amazon.science/blog/a-quick-guide-to-amazons-50-plus-papers-at-emnlp-2024" data-cms-ai="0" >A quick guide to Amazon’s 50-plus papers at EMNLP 2024</a> </div> <div class="PromoA-details"> <div class="PromoA-date">November 14, 2024</div> </div> <div class="PromoA-description">Large language models predominate, both as a research subject themselves and as tools for researching topics of particular interest to Amazon, such as speech, recommendations, and information retrieval.</div> <div class="PromoA-category"> <a class="Link" href="https://www.amazon.science/research-areas/conversational-ai-natural-language-processing" data-cms-ai="0" >Conversational AI</a> </div> </div> </div> </li> <li class="ListA-items-item"> <div class="PromoA" data-content-type="blog post" data-image-align="top" > <div class="PromoA-media"> <a class="Link" aria-label="Five ways the ABACUS label advances nature-based carbon removal" href="https://www.amazon.science/blog/five-ways-the-abacus-label-advances-nature-based-carbon-removal" data-cms-ai="0" ><picture><source type="image/webp" width="535" height="300" data-image-size="promoMedium" data-srcset="https://assets.amazon.science/dims4/default/90bf1ee/2147483647/strip/true/crop/1435x805+0+1/resize/535x300!/format/webp/quality/90/?url=http%3A%2F%2Famazon-topics-brightspot.s3.amazonaws.com%2Fscience%2Fed%2Fd6%2F76b1877a42dc908dbff060fc156c%2Fabacus.png"data-lazy-load="true" srcset='data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZlcnNpb249IjEuMSIgaGVpZ2h0PSIzMDBweCIgd2lkdGg9IjUzNXB4Ij48L3N2Zz4='/><source width="535" height="300" data-image-size="promoMedium" data-srcset="https://assets.amazon.science/dims4/default/090473c/2147483647/strip/true/crop/1435x805+0+1/resize/535x300!/quality/90/?url=http%3A%2F%2Famazon-topics-brightspot.s3.amazonaws.com%2Fscience%2Fed%2Fd6%2F76b1877a42dc908dbff060fc156c%2Fabacus.png"data-lazy-load="true" srcset='data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZlcnNpb249IjEuMSIgaGVpZ2h0PSIzMDBweCIgd2lkdGg9IjUzNXB4Ij48L3N2Zz4='/> <img class="Image" data-image-size="promoMedium" alt="ABACUS.png" width="535" height="300" data-src="https://assets.amazon.science/dims4/default/090473c/2147483647/strip/true/crop/1435x805+0+1/resize/535x300!/quality/90/?url=http%3A%2F%2Famazon-topics-brightspot.s3.amazonaws.com%2Fscience%2Fed%2Fd6%2F76b1877a42dc908dbff060fc156c%2Fabacus.png" data-lazy-load="true" src="data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZlcnNpb249IjEuMSIgaGVpZ2h0PSIzMDBweCIgd2lkdGg9IjUzNXB4Ij48L3N2Zz4="> </picture> </a> </div> <div class="PromoA-content"> <div class="PromoA-title"> <a class="Link" href="https://www.amazon.science/blog/five-ways-the-abacus-label-advances-nature-based-carbon-removal" data-cms-ai="0" >Five ways the ABACUS label advances nature-based carbon removal</a> </div> <div class="PromoA-details"> <div class="PromoA-date">November 11, 2024</div> </div> <div class="PromoA-description">From more-accurate measurement of carbon dioxide removal to greater diversity in restoration design, the ABACUS label’s requirements help advance the integrity of restoration projects in the voluntary carbon market.</div> <div class="PromoA-category"> <a class="Link" href="https://www.amazon.science/research-areas/sustainability" data-cms-ai="0" >Sustainability</a> </div> </div> </div> </li> </ul> </div> <ps-list-loadmore class="ListE" data-hexagon-collage="list-e" data-add-divider columnCount="3" > <div class="ListE-header-wrapper"> <h2 class="ListE-header">Publications</h2><a class="ListE-header-button" href="https://www.amazon.science/publications" data-cms-ai="0">View all</a> <a class="ListE-header-buttonDesktop" href="https://www.amazon.science/publications" data-cms-ai="0">View all</a> </div> <div class="ListE-body"> <ul class="ListE-items" data-list-loadmore-items> <li class="ListE-items-item"> <div class="PromoF" data-content-type="publication" data-no-media > <div class="PromoF-title"> <a class="Link" href="https://www.amazon.science/publications/chronos-learning-the-language-of-time-series" data-cms-ai="0" >Chronos: Learning the language of time series</a> </div> <div class="PromoF-details"> <div class="PromoF-authors"><a class="Link" href="https://www.amazon.science/author/abdul-fatir-ansari" data-cms-ai="0" >Abdul Fatir Ansari</a>, <a class="Link" href="https://www.amazon.science/author/lorenzo-stella" data-cms-ai="0" >Lorenzo Stella</a>, <a class="Link" href="https://www.amazon.science/author/caner-turkmen" data-cms-ai="0" >Caner Turkmen</a>, <a class="Link" href="https://www.amazon.science/author/xiyuan-zhang" data-cms-ai="0" >Xiyuan Zhang</a>, <a class="Link" href="https://www.amazon.science/author/pedro-mercado" data-cms-ai="0" >Pedro Mercado</a>, <a class="Link" href="https://www.amazon.science/author/huibin-shen" data-cms-ai="0" >Huibin Shen</a>, <a class="Link" href="https://www.amazon.science/author/oleksandr-shchur" data-cms-ai="0" >Oleksandr Shchur</a>, <a class="Link" href="https://www.amazon.science/author/syama-rangapuram" data-cms-ai="0" >Syama Rangapuram</a>, <span class="Link">Sebastian Pineda Arango</span>, <a class="Link" href="https://www.amazon.science/author/shubham-kapoor" data-cms-ai="0" >Shubham Kapoor</a>, <a class="Link" href="https://www.amazon.science/author/jasper-zschiegner" data-cms-ai="0" >Jasper Zschiegner</a>, <a class="Link" href="https://www.amazon.science/author/danielle-robinson" data-cms-ai="0" >Danielle Maddix Robinson</a>, <a class="Link" href="https://www.amazon.science/author/hao-wang" data-cms-ai="0" >Hao Wang</a>, <a class="Link" href="https://www.amazon.science/author/michael-mahoney" data-cms-ai="0" >Michael Mahoney</a>, <a class="Link" href="https://www.amazon.science/author/kari-torkkola" data-cms-ai="0" >Kari Torkkola</a>, <a class="Link" href="https://www.amazon.science/author/andrew-wilson" data-cms-ai="0" >Andrew Wilson</a>, <a class="Link" href="https://www.amazon.science/author/michael-bohlke-schneider" data-cms-ai="0" >Michael Bohlke-Schneider</a>, <a class="Link" href="https://www.amazon.science/author/bernie-wang" data-cms-ai="0" >Yuyang (Bernie) Wang</a></div> <div class="PromoF-journal"><span class="Link">Transactions of Machine Learning Research</span></div> <div class="PromoF-date">2024</div> </div> <div class="PromoF-content"> <div class="PromoF-body"> <div data-truncation-line-count="3" data-truncation-link-label="Read more" class="PromoF-description">We introduce Chronos, a simple yet effective framework for pretrained probabilistic time series models. Chronos tokenizes time series values using scaling and quantization into a fixed vocabulary and trains existing transformer-based language model architectures on these tokenized time series via the cross-entropy loss. We pretrained Chronos models based on the T5 family (ranging from 20M to 710M parameters</div> <div class="PromoF-category"> <a class="Link" href="https://www.amazon.science/research-areas/machine-learning" data-cms-ai="0" >Machine learning</a> </div> </div> </div> </div> </li> <li class="ListE-items-item"> <div class="PromoF" data-content-type="publication" data-no-media > <div class="PromoF-title"> <a class="Link" href="https://www.amazon.science/publications/windsorml-high-fidelity-computational-fluid-dynamics-dataset-for-automotive-aerodynamics" data-cms-ai="0" >WindsorML: High-fidelity computational fluid dynamics dataset for automotive aerodynamics</a> </div> <div class="PromoF-details"> <div class="PromoF-authors"><a class="Link" href="https://www.amazon.science/author/neil-ashton" data-cms-ai="0" >Neil Ashton</a>, <span class="Link">Jordan B. Angel</span>, <span class="Link">Aditya S. Ghate</span>, <span class="Link">Gaetan K. W. Kenway</span>, <span class="Link">Man Long Wong</span>, <span class="Link">Cetin Kiris</span>, <span class="Link">Astrid Walle</span>, <a class="Link" href="https://www.amazon.science/author/danielle-robinson" data-cms-ai="0" >Danielle Maddix Robinson</a>, <span class="Link">Gary Page</span></div> <div class="PromoF-journal"><a class="Link" href="https://www.amazon.science/conferences-and-events/neurips-2024" data-cms-ai="0" >NeurIPS 2024</a></div> <div class="PromoF-date">2024</div> </div> <div class="PromoF-content"> <div class="PromoF-body"> <div data-truncation-line-count="3" data-truncation-link-label="Read more" class="PromoF-description">This paper presents a new open-source high-fidelity dataset for Machine Learning (ML) containing 355 geometric variants of the Windsor body, to help the development and testing of ML surrogate models for external automotive aerodynamics. Each Computational Fluid Dynamics (CFD) simulation was run with a GPU-native high-fidelity Wall-Modeled Large-Eddy Simulations (WMLES) using a Cartesian immersed-boundary</div> <div class="PromoF-category"> <a class="Link" href="https://www.amazon.science/research-areas/machine-learning" data-cms-ai="0" >Machine learning</a> </div> </div> </div> </div> </li> <li class="ListE-items-item"> <div class="PromoF" data-content-type="publication" data-no-media > <div class="PromoF-title"> <a class="Link" href="https://www.amazon.science/publications/a-survey-on-knowledge-editing-of-neural-networks" data-cms-ai="0" >A survey on knowledge editing of neural networks</a> </div> <div class="PromoF-details"> <div class="PromoF-authors"><a class="Link" href="https://www.amazon.science/author/vittorio-mazzia" data-cms-ai="0" >Vittorio Mazzia</a>, <span class="Link">Alessandro Pedrani</span>, <a class="Link" href="https://www.amazon.science/author/andrea-caciolai" data-cms-ai="0" >Andrea Caciolai</a>, <a class="Link" href="https://www.amazon.science/author/kay-rottmann" data-cms-ai="0" >Kay Rottmann</a>, <a class="Link" href="https://www.amazon.science/author/davide-bernardi" data-cms-ai="0" >Davide Bernardi</a></div> <div class="PromoF-journal"><span class="Link">IEEE Transactions on Neural Networks and Learning Systems</span></div> <div class="PromoF-date">2024</div> </div> <div class="PromoF-content"> <div class="PromoF-body"> <div data-truncation-line-count="3" data-truncation-link-label="Read more" class="PromoF-description">Deep neural networks are becoming increasingly pervasive in academia and industry, matching and surpassing human performance on a wide variety of fields and related tasks. However, just as humans, even the largest artificial neural networks make mistakes, and once-correct predictions can become invalid as the world progresses in time. Augmenting datasets with samples that account for mistakes or up-to-date</div> <div class="PromoF-category"> <a class="Link" href="https://www.amazon.science/research-areas/machine-learning" data-cms-ai="0" >Machine learning</a> </div> </div> </div> </div> </li> <li class="ListE-items-item"> <div class="PromoF" data-content-type="publication" data-no-media > <div class="PromoF-title"> <a class="Link" href="https://www.amazon.science/publications/bpid-a-benchmark-for-personal-identity-deduplication" data-cms-ai="0" >BPID: A benchmark for personal identity deduplication</a> </div> <div class="PromoF-details"> <div class="PromoF-authors"><a class="Link" href="https://www.amazon.science/author/runhui-wang" data-cms-ai="0" >Runhui Wang</a>, <a class="Link" href="https://www.amazon.science/author/yefan-tao" data-cms-ai="0" >Yefan Tao</a>, <a class="Link" href="https://www.amazon.science/author/adit-krishnan" data-cms-ai="0" >Adit Krishnan</a>, <a class="Link" href="https://www.amazon.science/author/chris-luyang-kong" data-cms-ai="0" >Chris (Luyang) Kong</a>, <a class="Link" href="https://www.amazon.science/author/xuanqing-liu" data-cms-ai="0" >Xuanqing Liu</a>, <a class="Link" href="https://www.amazon.science/author/yuqian-deng" data-cms-ai="0" >Yuqian Deng</a>, <a class="Link" href="https://www.amazon.science/author/yunzhao-yang" data-cms-ai="0" >Yunzhao Yang</a>, <a class="Link" href="https://www.amazon.science/author/Henrik-Johnson" data-cms-ai="0" >Henrik Johnson</a>, <a class="Link" href="https://www.amazon.science/author/andrew-borthwick" data-cms-ai="0" >Andrew Borthwick</a>, <a class="Link" href="https://www.amazon.science/author/aditi-sinha" data-cms-ai="0" >Aditi Sinha</a>, <a class="Link" href="https://www.amazon.science/author/Davor-Golac" data-cms-ai="0" >Davor Golac</a></div> <div class="PromoF-journal"><a class="Link" href="https://www.amazon.science/conferences-and-events/emnlp-2024" data-cms-ai="0" >EMNLP 2024</a></div> <div class="PromoF-date">2024</div> </div> <div class="PromoF-content"> <div class="PromoF-body"> <div data-truncation-line-count="3" data-truncation-link-label="Read more" class="PromoF-description">Data deduplication is a critical task in data management and mining, focused on consolidating duplicate records that refer to the same entity. Personally Identifiable Information (PII) is a critical class of data for deduplication across various industries. Consumer data, stored and generated through various engagement channels, is crucial for marketers, agencies, and publishers. However, a major challenge</div> <div class="PromoF-category"> <a class="Link" href="https://www.amazon.science/research-areas/information-and-knowledge-management" data-cms-ai="0" >Information and knowledge management</a> </div> </div> </div> </div> </li> <li class="ListE-items-item"> <div class="PromoF" data-content-type="publication" data-no-media > <div class="PromoF-title"> <a class="Link" href="https://www.amazon.science/publications/improving-tool-retrieval-by-leveraging-large-language-models-for-query-generation" data-cms-ai="0" >Improving tool retrieval by leveraging large language models for query generation</a> </div> <div class="PromoF-details"> <div class="PromoF-authors"><span class="Link">Mohammad Kachuee</span>, <a class="Link" href="https://www.amazon.science/author/sarthak-ahuja" data-cms-ai="0" >Sarthak Ahuja</a>, <a class="Link" href="https://www.amazon.science/author/vaibhav-kumar-1" data-cms-ai="0" >Vaibhav Kumar</a>, <a class="Link" href="https://www.amazon.science/author/puyang-xu" data-cms-ai="0" >Puyang Xu</a>, <a class="Link" href="https://www.amazon.science/author/derek-liu" data-cms-ai="0" >Derek Liu</a></div> <div class="PromoF-journal"><span class="Link">COLING 2025</span></div> <div class="PromoF-date">2024</div> </div> <div class="PromoF-content"> <div class="PromoF-body"> <div data-truncation-line-count="3" data-truncation-link-label="Read more" class="PromoF-description">Using tools by Large Language Models (LLMs) is a promising avenue to extend their reach beyond language or conversational settings. The number of tools can scale to thousands as they enable accessing sensory information, fetching updated factual knowledge, or taking actions in the real world. In such settings, in-context learning by providing a short list of relevant tools in the prompt is a viable approach</div> <div class="PromoF-category"> <a class="Link" href="https://www.amazon.science/research-areas/conversational-ai-natural-language-processing" data-cms-ai="0" >Conversational AI</a> </div> </div> </div> </div> </li> </ul> <div class="ListE-nextPage" data-list-loadmore-pagination> <a class="Link" href="?0000016e-8f90-d381-abee-cfb5f49d0000-page=2" data-cms-ai="0">Load more</a> </div> </div> </ps-list-loadmore> <div class="ListB" data-add-divider columnCount="3" > <div class="ListB-header-wrapper"> <h2 class="ListB-header">Resources</h2> </div> <ul class="ListB-items" data-list-loadmore-items> <li class="ListB-items-item"> <div class="PromoA" data-image-align="top" > <div class="PromoA-media"> <a class="Link" aria-label="Career opportunities" href="https://www.amazon.science/careers" data-cms-ai="0" ><picture><source type="image/webp" width="535" height="300" 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srcset='data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZlcnNpb249IjEuMSIgaGVpZ2h0PSIzMDBweCIgd2lkdGg9IjUzNXB4Ij48L3N2Zz4='/> <img class="Image" data-image-size="promoMedium" alt="amazon-science-6 copy.jpg" width="535" height="300" data-src="https://assets.amazon.science/dims4/default/3613755/2147483647/strip/true/crop/2201x1234+98+281/resize/535x300!/quality/90/?url=http%3A%2F%2Famazon-topics-brightspot.s3.amazonaws.com%2Fscience%2F7c%2Fc7%2Fe60ea268404997738e7d1ea46aef%2Famazon-science-6-copy.jpg" data-lazy-load="true" src="data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZlcnNpb249IjEuMSIgaGVpZ2h0PSIzMDBweCIgd2lkdGg9IjUzNXB4Ij48L3N2Zz4="> </picture> </a> </div> <div class="PromoA-content"> <div class="PromoA-title"> <a class="Link" href="https://www.amazon.science/careers" data-cms-ai="0" >Career opportunities</a> </div> <div class="PromoA-details"> </div> <div class="PromoA-description">We look for talent from around the world for applied scientists, data scientists, economists, research scientists, scholars, academics, PhDs, and interns.</div> </div> </div> </li> <li class="ListB-items-item"> <div class="PromoA" data-content-type="page" data-image-align="top" > <div class="PromoA-media"> <a class="Link" aria-label="Academic collaborations" href="https://www.amazon.science/academic-engagements" data-cms-ai="0" ><picture><source type="image/webp" width="535" height="300" data-image-size="promoMedium" data-srcset="https://assets.amazon.science/dims4/default/c7e2f3e/2147483647/strip/true/crop/1423x798+201+154/resize/535x300!/format/webp/quality/90/?url=http%3A%2F%2Famazon-topics-brightspot.s3.amazonaws.com%2Fscience%2F0e%2F5f%2F7f1d7a1f40d7806918af66e2122b%2Fdownload-18.jpeg"data-lazy-load="true" srcset='data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZlcnNpb249IjEuMSIgaGVpZ2h0PSIzMDBweCIgd2lkdGg9IjUzNXB4Ij48L3N2Zz4='/><source width="535" height="300" data-image-size="promoMedium" data-srcset="https://assets.amazon.science/dims4/default/6f31fc3/2147483647/strip/true/crop/1423x798+201+154/resize/535x300!/quality/90/?url=http%3A%2F%2Famazon-topics-brightspot.s3.amazonaws.com%2Fscience%2F0e%2F5f%2F7f1d7a1f40d7806918af66e2122b%2Fdownload-18.jpeg"data-lazy-load="true" srcset='data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZlcnNpb249IjEuMSIgaGVpZ2h0PSIzMDBweCIgd2lkdGg9IjUzNXB4Ij48L3N2Zz4='/> <img class="Image" data-image-size="promoMedium" alt="download (18).jpeg" width="535" height="300" data-src="https://assets.amazon.science/dims4/default/6f31fc3/2147483647/strip/true/crop/1423x798+201+154/resize/535x300!/quality/90/?url=http%3A%2F%2Famazon-topics-brightspot.s3.amazonaws.com%2Fscience%2F0e%2F5f%2F7f1d7a1f40d7806918af66e2122b%2Fdownload-18.jpeg" data-lazy-load="true" src="data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZlcnNpb249IjEuMSIgaGVpZ2h0PSIzMDBweCIgd2lkdGg9IjUzNXB4Ij48L3N2Zz4="> </picture> </a> </div> <div class="PromoA-content"> <div class="PromoA-title"> <a class="Link" href="https://www.amazon.science/academic-engagements" data-cms-ai="0" >Academic collaborations</a> </div> <div class="PromoA-details"> </div> <div class="PromoA-description">We collaborate with leading academic organizations to drive innovation and to ensure that research is creating solutions whose benefits are shared broadly.</div> </div> </div> </li> <li class="ListB-items-item"> <div class="PromoA" data-content-type="page" data-image-align="top" > <div class="PromoA-media"> <a class="Link" aria-label="Awards and recognitions" href="https://www.amazon.science/awards-and-recognitions" data-cms-ai="0" ><picture><source type="image/webp" width="535" height="300" data-image-size="promoMedium" data-srcset="https://assets.amazon.science/dims4/default/c3eb2cf/2147483647/strip/true/crop/767x430+0+16/resize/535x300!/format/webp/quality/90/?url=http%3A%2F%2Famazon-topics-brightspot.s3.amazonaws.com%2Fscience%2Fd8%2F6e%2F25c525e84266abb4ee7d718b7ac6%2Famazon-science-awards-recognitions.jpeg"data-lazy-load="true" srcset='data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZlcnNpb249IjEuMSIgaGVpZ2h0PSIzMDBweCIgd2lkdGg9IjUzNXB4Ij48L3N2Zz4='/><source width="535" height="300" data-image-size="promoMedium" data-srcset="https://assets.amazon.science/dims4/default/65055b0/2147483647/strip/true/crop/767x430+0+16/resize/535x300!/quality/90/?url=http%3A%2F%2Famazon-topics-brightspot.s3.amazonaws.com%2Fscience%2Fd8%2F6e%2F25c525e84266abb4ee7d718b7ac6%2Famazon-science-awards-recognitions.jpeg"data-lazy-load="true" srcset='data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZlcnNpb249IjEuMSIgaGVpZ2h0PSIzMDBweCIgd2lkdGg9IjUzNXB4Ij48L3N2Zz4='/> <img class="Image" data-image-size="promoMedium" alt="Amazon Science Awards Recognitions.jpeg" width="535" height="300" data-src="https://assets.amazon.science/dims4/default/65055b0/2147483647/strip/true/crop/767x430+0+16/resize/535x300!/quality/90/?url=http%3A%2F%2Famazon-topics-brightspot.s3.amazonaws.com%2Fscience%2Fd8%2F6e%2F25c525e84266abb4ee7d718b7ac6%2Famazon-science-awards-recognitions.jpeg" data-lazy-load="true" src="data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZlcnNpb249IjEuMSIgaGVpZ2h0PSIzMDBweCIgd2lkdGg9IjUzNXB4Ij48L3N2Zz4="> </picture> </a> <div class="PromoA-media-credit">Photo by Zak Brickett</div> </div> <div class="PromoA-content"> <div class="PromoA-title"> <a class="Link" href="https://www.amazon.science/awards-and-recognitions" data-cms-ai="0" >Awards and recognitions</a> </div> <div class="PromoA-details"> </div> <div class="PromoA-description">Learn more about the awards and recognitions that Amazon researches from around the world have been honored with during their tenure.</div> </div> </div> </li> </ul> </div> </main> <div class="Page-below" ><ps-carousel class="ListI" columnCount="3" > <div class="ListI-header-wrapper"> <h2 class="ListI-header">Work with us</h2><a class="ListI-header-button" href="https://www.amazon.science/careers" data-cms-ai="0">See more jobs</a> <a class="ListI-header-buttonDesktop" href="https://www.amazon.science/careers" data-cms-ai="0">See more jobs</a> </div> <div class="ListI-mask"> <div class="ListI-slides"> <div class="ListI-slide"> <div class="PromoA" data-no-media data-image-align="top" > <div class="PromoA-content"> <div class="PromoA-title"> <a class="Link" href="https://www.amazon.jobs/jobs/2840061/applied-scientist-sponsored-products-amazon-ads?cmpid=bsp-amazon-science" target="_blank" data-cms-ai="0" >Applied Scientist, Sponsored Products, Amazon Ads</a> </div> <div class="PromoA-details"> <div class="PromoA-location">US, NY, New York</div> </div> <div data-truncation-line-count="5" data-truncation-link-label="Read more" class="PromoA-description">Amazon continues to invest heavily in building our world class advertising business. Our products are strategically important to our Retail and Marketplace businesses, driving long term growth. We deliver billions of ad impressions and millions of clicks daily, breaking fresh ground to create world-class products. We are highly motivated, collaborative and fun-loving with an entrepreneurial spirit and strong bias for action. With a broad mandate to experiment and innovate, we are growing at an unprecedented rate with a seemingly endless range of new opportunities.The Sponsored Products OSSR team is responsible for all non-search supply and associated experiences. As an Applied Scientist on our team, you will be responsible for defining the science and technical strategy for one of our most impactful strategic initiatives, creating lasting value for Amazon and our advertising customers. Key job responsibilities • Support business, science and engineering strategy and roadmap for Sponsored Products OSSR projects • Drive alignment across organizations for science, engineering and product strategy to achieve business goals • Lead/guide scientists and engineers across teams to develop, test, launch and improve of science models designed to optimize the shopper experience and deliver long term value for Amazon and advertisers • Develop state of the art experimental approaches and ML models. About the team Sponsored Products (SP) is Amazon's largest and fastest growing business. Over the last few years we grown to a multi-billion dollar business. SP ads are shown prominently throughout search and detail pages, allowing shoppers to seamlessly discover products sold on Amazon. Ad experience and market place is one of the highest impact decisions we make. This role has unparalleled opportunity to grow our marketplace and deliver value for advertisers and shoppers.</div> </div> </div> </div> <div class="ListI-slide"> <div class="PromoA" data-no-media data-image-align="top" > <div class="PromoA-content"> <div class="PromoA-title"> <a class="Link" href="https://www.amazon.jobs/jobs/2840111/applied-scientist-machine-learning-accelerator?cmpid=bsp-amazon-science" target="_blank" data-cms-ai="0" >Applied Scientist, Machine Learning Accelerator</a> </div> <div class="PromoA-details"> <div class="PromoA-location">US, WA, Seattle</div> </div> <div data-truncation-line-count="5" data-truncation-link-label="Read more" class="PromoA-description">Do you want to join an innovative team of scientists who use deep learning, natural language processing, large language models to help Amazon provide the best seller experience across the entire Seller life cycle, including recruitment, growth, support and provide the best customer and seller experience by automatically mitigating risk? Do you want to build advanced algorithmic systems that help manage the trust and safety of millions of customer interactions every day? Are you excited by the prospect of analyzing and modeling terabytes of data and creating state-of-the-art algorithms to solve real world problems? Are you excited by the opportunity to leverage GenAI and innovate on top of the state-of-the-art large language models to improve customer and seller experience? Do you like to build end-to-end business solutions and directly impact the profitability of the company? Do you like to innovate and simplify processes? If yes, then you may be a great fit to join the Machine Learning Accelerator team in the Amazon Selling Partner Services (SPS) group. Key job responsibilities The scope of an Applied Scientist II in the Selling Partner Services (SPS) Machine Learning Accelerator (MLA) team is to research and prototype Machine Learning applications that solve strategic business problems across SPS domains. Additionally, the scientist collaborates with engineers and business partners to design and implement solutions at scale when they are determined to be of broad benefit to SPS organizations. They develop large-scale solutions for high impact projects, introduce tools and other techniques that can be used to solve problems from various perspectives, and show depth and competence in more than one area. They influence the team’s technical strategy by making insightful contributions to the team’s priorities, approach and planning. They develop and introduce tools and practices that streamline the work of the team, and they mentor junior team members and participate in hiring.</div> </div> </div> </div> <div class="ListI-slide"> <div class="PromoA" data-no-media data-image-align="top" > <div class="PromoA-content"> <div class="PromoA-title"> <a class="Link" href="https://www.amazon.jobs/jobs/2839736/applied-scientist-amazon?cmpid=bsp-amazon-science" target="_blank" data-cms-ai="0" >Applied Scientist, Amazon</a> </div> <div class="PromoA-details"> <div class="PromoA-location">US, WA, Seattle</div> </div> <div data-truncation-line-count="5" data-truncation-link-label="Read more" class="PromoA-description">An information-rich and accurate product catalog is a strategic asset for Amazon. It powers unrivaled product discovery, informs customer buying decisions, offers a large selection, and positions Amazon as the first stop for shopping online. We use data analysis and statistical and machine learning techniques to proactively identify relationships between products within the Amazon product catalog. This problem is challenging due to sheer scale (billions of products in the catalog), diversity (products ranging from electronics to groceries to instant video across multiple languages) and multitude of input sources (millions of sellers contributing product data with different quality). Amazon’s Item and Relationship Identity Systems group is looking for an innovative and customer-focused applied scientist to help us make the world’s best product catalog even better. In this role, you will partner with technology and business leaders to build new state-of-the-art algorithms, models, and services to infer product-to-product relationships that matter to our customers. You will work in a collaborative environment where you can experiment with massive data from the world’s largest product catalog, work on challenging problems, quickly implement and deploy your algorithmic ideas at scale, understand whether they succeed via statistically relevant experiments across millions of customers. Key job responsibilities * Map business requirements and customer needs to a scientific problem. * Align the research direction to business requirements and make the right judgments on research/development schedule and prioritization. * Research, design and implement scalable machine learning (ML) techniques to solve problems that matter to our customers in an iterative fashion. * Design, experiment and evaluate highly innovative models for predictive, explainable learning * Partner with other scientists to build state-of-the-art ML systems powering Amazon * Work closely with software engineering teams to drive real-time model experiments, implementations and new feature creations * Stay informed on the latest machine learning, natural language and/or artificial intelligence trends and make presentations to the larger engineering and applied science communities. About the team The IRIS team owns programs and systems to ensure uniqueness and consistency of product identity and to infer relationships between products in Amazon Catalog. We focus on the following areas: 1) reducing customer perceived duplicates: eliminating all duplicate ASINs that are indistinguishable by customers and identifying broken and missing variations, 2) reducing product detail page inconsistency: preventing inconsistent item identities, and improving the customer experience by automatically detecting and creating factual relationships between ASINs: e.g. variation families, newer versions, 3) reducing selling partner listing friction: reducing GTIN defects in the catalog, and false conflicts in contributions, and 4) improving brand customer experience: providing a strong brand identity to contributions and ASINs, by matching them to Universal Brand Catalog brand entities.</div> </div> </div> </div> <div class="ListI-slide"> <div class="PromoA" data-no-media data-image-align="top" > <div class="PromoA-content"> <div class="PromoA-title"> <a class="Link" href="https://www.amazon.jobs/jobs/2840075/applied-scientist--amazon?cmpid=bsp-amazon-science" target="_blank" data-cms-ai="0" >Applied Scientist , Amazon</a> </div> <div class="PromoA-details"> <div class="PromoA-location">US, WA, Seattle</div> </div> <div data-truncation-line-count="5" data-truncation-link-label="Read more" class="PromoA-description">Amazon Advertising operates at the intersection of eCommerce and advertising, and is investing heavily in building a world-class advertising business. We are defining and delivering a collection of self-service performance advertising products that drive discovery and sales. Our products are strategically important to our Retail and Marketplace businesses driving long-term growth. We deliver billions of ad impressions and millions of clicks daily and are breaking fresh ground to create world-class products to improve both shopper and advertiser experience. With a broad mandate to experiment and innovate, we grow at an unprecedented rate with a seemingly endless range of new opportunities. The Ad Response Prediction team in Sponsored Products organization build advanced deep-learning models, large-scale machine-learning pipelines, and real-time serving infra to match shoppers’ intent to relevant ads on all devices, for all contexts and in all marketplaces. Through precise estimation of shoppers’ interaction with ads and their long-term value, we aim to drive optimal ads allocation and pricing, and help to deliver a relevant, engaging and delightful ads experience to Amazon shoppers. As the business and the complexity of various new initiatives we take continues to grow, we are looking for talented Applied Scientists to join the team. Key job responsibilities As a Applied Scientist II, you will: * Conduct hands-on data analysis, build large-scale machine-learning models and pipelines * Work closely with software engineers on detailed requirements, technical designs and implementation of end-to-end solutions in production * Run regular A/B experiments, gather data, perform statistical analysis, and communicate the impact to senior management * Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving * Provide technical leadership, research new machine learning approaches to drive continued scientific innovation * Be a member of the Amazon-wide Machine Learning Community, participating in internal and external MeetUps, Hackathons and Conferences</div> </div> </div> </div> <div class="ListI-slide"> <div class="PromoA" data-no-media data-image-align="top" > <div class="PromoA-content"> <div class="PromoA-title"> <a class="Link" href="https://www.amazon.jobs/jobs/2840082/genaiic-geo-leader-awsi-generative-ai-innovation-center?cmpid=bsp-amazon-science" target="_blank" data-cms-ai="0" >GenAIIC Geo Leader, AWSI, Generative AI Innovation Center</a> </div> <div class="PromoA-details"> <div class="PromoA-location">US, VA, Arlington</div> </div> <div data-truncation-line-count="5" data-truncation-link-label="Read more" class="PromoA-description">Are you passionate about helping customers innovate and create transformative experiences? Do you want to be at the forefront of utilizing AI and ML services to drive innovation for thousands of AWS customers globally? And are you interested in building and leading high-performing teams in the fast paced area of generative AI? The AWS Generative AI Innovation Center (GenAIIC) is on a mission to accelerate production adoption of AWS generative AI services across global customers. This is an exciting opportunity to be part of a fast growing and entrepreneurial organization, bringing innovation to customers and growing the adoption of cloud services. The GenAIIC organization is staffed with specialized and experienced business strategists and ML scientists who solve complex problems for customers. We start with the customer and work backwards in everything we do. We believe that GenAI solutions, when founded in deep customer needs, can transform organizations, and produce groundbreaking experiences. We are a small, tight-knit team who value authentic, teammates that think creatively and will proactively seek out opportunities to advance the impact of the GenAIIC. As the GenAIIC Geo Leader, AWSI, you will be responsible for building and managing a team of generative AI strategists and ML scientists that are working with large multi-national customers to architect and implement innovative generative AI solutions. You will regularly engage with Director, VP, C-level customer executives. You will be responsible for building relationships, capturing opportunity, driving engagement, and delivering on business objectives. You must bring a strong blend of business and technical skills and lead all GenAIIC activities for AWS Industries. You are ready to roll-up your sleeves and work alongside the team in developing solutions when needed. You will lean on your experience working directly with customers, and are passionate in empowering these organizations to leverage generative AI and derive business outcomes. Key job responsibilities - Lead a high-performing team of business strategists, ML scientists, and generative AI domain experts - Guide the team in designing, developing, and evaluating innovative generative AI solutions to address customer issues - Advise and influence customers on technology decisions, including contrasting technology choices - Drive adoption of AWS generative AI services, delivering workload/solution specific domain expertise to the broader field organization, and developing repeatable, packaged solutions - Develop new strategies and mechanisms to address evolving customer needs, while driving higher business impact for AWS - Influence product roadmap with service teams to deliver value for customers and accelerate adoption About the team About AWS Diverse Experiences AWS values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Why AWS? Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Inclusive Team Culture Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness. Mentorship & Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve.</div> </div> </div> </div> <div class="ListI-slide"> <div class="PromoA" data-no-media data-image-align="top" > <div class="PromoA-content"> <div class="PromoA-title"> <a class="Link" href="https://www.amazon.jobs/jobs/2838745/senior-applied-scientist-amazon?cmpid=bsp-amazon-science" target="_blank" data-cms-ai="0" >Senior Applied Scientist, Amazon</a> </div> <div class="PromoA-details"> <div class="PromoA-location">US, NY, New York</div> </div> <div data-truncation-line-count="5" data-truncation-link-label="Read more" class="PromoA-description">We are seeking a motivated and experienced Senior Applied Scientist with expertise in Machine Learning (ML), Artificial Intelligence (AI), Big Data, and Service Oriented Architecture. You should have a deep understanding of the digital advertising business and scaled marketing across communication channels. In this role, you will collaborate with a cross-functional team of talented scientists and engineers to innovate, iterate, and solve real-world marketing problems with cutting-edge AWS technologies. You will lead in-depth analyses of the key problems faced by Amazon Ads customers and the challenges faced by marketing teams in meeting customer needs at scale. To address these problems, you will build innovative large-scale ML/AI solutions such as bespoke omni-channel recommender systems, and specialized LLM-powered assistants for customers and marketers. You will independently drive research and prototyping to deliver functional proofs of concept (POCs), and then partner with engineers to inform the technology roadmap and deploy successful POCs as scalable batch and real-time applications in production. Key job responsibilities • Define and execute a research and development plan that enables data-driven marketing decisions and delivers inspiring customer experiences • Evaluate, evolve, and invent scientific techniques to effectively address customer needs and business problems • Establish and drive science prototyping best practices to ensure coherence and integrity of data feeding into production ML/AI solutions • Collaborate with colleagues across science and engineering disciplines for rapid prototyping at scale • Partner with engineering teams to solve complex technical problems, define system-level requirements, develop implementation plans, and guide the adaptation of techniques to meet production needs • Partner with product managers and stakeholders to define forward-looking product visions and prospective business use-cases • Drive and lead of culture of data-driven innovation within and outside across Amazon Ads Marketing organization • Influence organizational vision across Ads Marketing organization About the team The Marketing Decisions Science team provides AI/ML products to enable Amazon Ads Marketing to deliver relevant and compelling guidance, education, and inspiration to prospective and active advertisers across marketing channels. We own the product, technology, and deployment roadmap for AI/ML products across Amazon Ads Marketing. We analyze the needs, experiences, and behaviors of Amazon advertisers at petabytes scale, to deliver the right marketing communications to the right advertiser at the right time. Our products enable applications and synergies across Ads organization, spanning marketing, product, and sales use cases.</div> </div> </div> </div> <div class="ListI-slide"> <div class="PromoA" data-no-media data-image-align="top" > <div class="PromoA-content"> <div class="PromoA-title"> <a class="Link" href="https://www.amazon.jobs/jobs/2838752/sr-data-scientist-generative-ai-innovation-center?cmpid=bsp-amazon-science" target="_blank" data-cms-ai="0" >Sr. Data Scientist, Generative AI Innovation Center</a> </div> <div class="PromoA-details"> <div class="PromoA-location">US, NY, New York</div> </div> <div data-truncation-line-count="5" data-truncation-link-label="Read more" class="PromoA-description">Are you looking to work at the forefront of Machine Learning and AI? Would you be excited to apply cutting edge Generative AI algorithms to solve real world problems with significant impact? The Generative AI Innovation Center at AWS is a new strategic team that helps AWS customers implement Generative AI solutions and realize transformational business opportunities. This is a team of strategists, data scientists, engineers, and solution architects working step-by-step with customers to build bespoke solutions that harness the power of generative AI. The team helps customers imagine and scope the use cases that will create the greatest value for their businesses, select and train and fine tune the right models, define paths to navigate technical or business challenges, develop proof-of-concepts, and make plans for launching solutions at scale. The GenAI Innovation Center team provides guidance on best practices for applying generative AI responsibly and cost efficiently. You will work directly with customers and innovate in a fast-paced organization that contributes to game-changing projects and technologies. You will design and run experiments, research new algorithms, and find new ways of optimizing risk, profitability, and customer experience. We’re looking for Data Scientists capable of using GenAI and other techniques to design, evangelize, and implement state-of-the-art solutions for never-before-solved problems. This position requires that the candidate selected be a US Citizen. Key job responsibilities As an Data Scientist, you will - Collaborate with AI/ML scientists and architects to Research, design, develop, and evaluate cutting-edge generative AI algorithms to address real-world challenges - Interact with customers directly to understand the business problem, help and aid them in implementation of generative AI solutions, deliver briefing and deep dive sessions to customers and guide customer on adoption patterns and paths to production - Create and deliver best practice recommendations, tutorials, blog posts, sample code, and presentations adapted to technical, business, and executive stakeholder - Provide customer and market feedback to Product and Engineering teams to help define product direction A day in the life About AWS Diverse Experiences AWS values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Why AWS? Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Inclusive Team Culture Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness. Mentorship & Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why flexible work hours and arrangements are part of our culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud.</div> </div> </div> </div> <div class="ListI-slide"> <div class="PromoA" data-no-media data-image-align="top" > <div class="PromoA-content"> <div class="PromoA-title"> <a class="Link" href="https://www.amazon.jobs/jobs/2839825/applied-scientist-sponsored-products?cmpid=bsp-amazon-science" target="_blank" data-cms-ai="0" >Applied Scientist, Sponsored Products</a> </div> <div class="PromoA-details"> <div class="PromoA-location">US, NY, New York</div> </div> <div data-truncation-line-count="5" data-truncation-link-label="Read more" class="PromoA-description">Amazon Ads is one of Amazon's fastest growing and most profitable businesses. As a core product offering within our advertising portfolio, Sponsored Products (SP) helps merchants, retail vendors, and brand owners succeed via native advertising, which grows incremental sales of their products sold through Amazon. The SP team's primary goals are to help shoppers discover new products they love, be the most efficient way for advertisers to meet their business objectives, and build a sustainable business that continuously innovates on behalf of customers. Our products and solutions are strategically important to drive long-term growth. We deliver billions of ad impressions and millions of clicks and break fresh ground in product and technical innovations every day! The Bespoke Shopping Experience team within SP develops customer facing experiences and machine learning models to better understand and address the diverse needs and behaviors of various shopper cohorts. As an Applied Scientist on the team, you will: - Drive end-to-end Machine Learning projects that have a high degree of ambiguity, scale, complexity. - Perform hands-on analysis and modeling of enormous data sets to develop insights that increase traffic monetization and merchandise sales, without compromising the shopper experience. - Build machine learning models, perform proof-of-concept, experiment, optimize, and deploy your models into production; work closely with software engineers to assist in productionizing your ML models. - Run A/B experiments, gather data, and perform statistical analysis. - Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving. - Research new and innovative machine learning approaches.</div> </div> </div> </div> <div class="ListI-slide"> <div class="PromoA" data-no-media data-image-align="top" > <div class="PromoA-content"> <div class="PromoA-title"> <a class="Link" href="https://www.amazon.jobs/jobs/2839664/senior-research-scientist-aigc-science-and-analytics?cmpid=bsp-amazon-science" target="_blank" data-cms-ai="0" >Senior Research Scientist, AIGC Science and Analytics</a> </div> <div class="PromoA-details"> <div class="PromoA-location">US, NY, New York</div> </div> <div data-truncation-line-count="5" data-truncation-link-label="Read more" class="PromoA-description">The PXT (People Experience and Technology) Science and Analytics team for AIGC (Ads, IMDb and Grand Challenge) is seeking a highly skilled and motivated Senior Research Scientist to join our team. You will be leading Research Science space to support the AIGC PXT org initiatives. If you enjoy innovating, thinking big and want to contribute directly to the success of a growing team, you may be a prime candidate for this position. Key job responsibilities In this role you will: Design experiments, test hypotheses, and build actionable models Conduct quantitative analyses of talent management data and trends Conduct qualitative data collection and analysis Partner closely and drive effective collaborations across multi-disciplinary research and product teams Consult on appropriate analytic methodologies and scope research requests</div> </div> </div> </div> <div class="ListI-slide"> <div class="PromoA" data-no-media data-image-align="top" > <div class="PromoA-content"> <div class="PromoA-title"> <a class="Link" href="https://www.amazon.jobs/jobs/2838848/research-scientist-prime-video?cmpid=bsp-amazon-science" target="_blank" data-cms-ai="0" >Research Scientist, Prime Video</a> </div> <div class="PromoA-details"> <div class="PromoA-location">US, WA, Seattle</div> </div> <div data-truncation-line-count="5" data-truncation-link-label="Read more" class="PromoA-description">Prime Video is a first-stop entertainment destination offering customers a vast collection of premium programming in one app available across thousands of devices. On Prime Video, customers can find their favorite movies, series, documentaries, and live sports – including Amazon MGM Studios-produced series and movies Road House, The Lord of the Rings: The Rings of Power, Fallout, Reacher, The Boys, and The Idea of You; licensed fan favorites Dawson’s Creek and IF; Prime member exclusive access to coverage of live sports including Thursday Night Football, WNBA, and NWSL, and acclaimed sports documentaries including Bye Bye Barry and Federer; and programming from partners such as Apple TV+, Max, Crunchyroll, and MGM+ via Prime Video add-on subscriptions, as well as more than 500 free ad-supported (FAST) Channels. Prime members in the U.S. can share a variety of benefits, including Prime Video, by using Amazon Household. Prime Video is one benefit among many that provides savings, convenience, and entertainment as part of the Prime membership. All customers, regardless of whether they have a Prime membership or not, can rent or buy titles, including blockbusters such as Challengers and The Fall Guy, via the Prime Video Store, and can enjoy content such as Jury Duty and Bosch: Legacy free with ads on Freevee. Customers can also go behind the scenes of their favorite movies and series with exclusive X-Ray access. For more info visit www.amazon.com/primevideo. Are you interested in shaping the future of entertainment? Prime Video's technology teams are creating best-in-class digital video experience. As a Prime Video technologist, you’ll have end-to-end ownership of the product, user experience, design, and technology required to deliver state-of-the-art experiences for our customers. You’ll get to work on projects that are fast-paced, challenging, and varied. You’ll also be able to experiment with new possibilities, take risks, and collaborate with remarkable people. We’ll look for you to bring your diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. With global opportunities for talented technologists, you can decide where a career Prime Video Tech takes you! Key job responsibilities As a Research Scientist at Prime Video, you will have end-to-end ownership of the product, related research and experimentation, applying advanced machine learning techniques in computer vision (CV), natural language processing (NLP), multimedia understanding and so on. You’ll work on diverse projects that enhance Prime Video’s recommendation systems, image/video understanding, and content personalization, driving impactful innovations for our global audience. Other responsibilities include: • Lead cutting-edge research in computer vision and natural language processing, applying it to video-centric media challenges. • Develop scalable machine learning models to enhance media asset generation, content discovery, and personalization. • Collaborate closely with engineering teams to integrate your models into production systems at scale, ensuring optimal performance and reliability. • Actively participate in publishing your research in leading conferences and journals. • Lead a team of skilled research scientists, you will shape the research strategy, create forward-looking roadmaps, and effectively communicate progress and insights to senior leadership • Stay up-to-date with the latest advancements in AI and machine learning to drive future research initiatives.</div> </div> </div> </div> </div> </div> </ps-carousel></div> </div> <ps-footer class="Page-footer"> <div class="Page-footer-body"> <div class="Page-footer-logo"><a aria-label="home 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