Subscribe
Follow Us
Washington, D.C.
Conference
KDD 2022
August 14 - 18, 2022
Washington, DC

Overview

The Knowledge Discovery and Data Mining Conference brings together researchers and practitioners from data science, data mining, knowledge discovery, large-scale data analytics, and big data.

Sponsorship Details

Gold
Conference website
Related research
Booth 145
Connect with us

Amazon organizing committee members

  • Chandan Reddy.jpg
    Keynote Speaker
    Chandan K. Reddy
    Amazon Scholar
    Plenary keynote speaker

    • Accepted publications

    Workshops

    KDD 2022 Workshop on Automation in Machine Learning
    August 14 - August 17
    Amazon workshop chairs: Jun (Luke) Huan, Tao Wang

    Website: https://sites.google.com/view/automl2022-workshop
    KDD 2022 Workshop on Mining and Learning with Graphs
    August 15
    The workshop serves as a forum for researchers from a variety of fields working on mining and learning from graphs to share and discuss their latest findings.

    Amazon organizer: Shobeir Fakhraei

    Website: http://www.mlgworkshop.org/2022
    ColdGuess: A general and effective relational graph convolutional network to tackle cold start cases
    Bo He, Xiang Song, Vincent Gao, Christos Faloutsos
    2022
    Machine learning
    KDD 2022 Workshop on AI for Climate Mitigation, Adaptation, and Environmental Justice
    August 15 - May 15
    Since 2016, the Fragile Earth Workshop has brought together the research community to find and explore how data science can measure and progress climate and social issues, following the framework of the United Nations Sustainable Development Goals (SDGs).

    Amazon organizer: Emre Eftelioglu

    Website: https://ai4good.org/fragile-earth-2022
    KDD 2022 Workshop on Misinformation and Misbehavior Mining on the Web & Making a Credible Web for Tomorrow
    August 15
    MIS2-TrueFact@KDD 2022 provides a venue for researchers and practitioners from academia, government and industry can share insights and identify new challenges and opportunities in adjacent to these diverse areas to coalesce around central and timely topics in online misinformation and misbehavior, resolving conflicts, fact-checking and ascertaining credibility of claims and present recent advances in research.

    Amazon organizer: Mehran Kafai

    Website: http://claws.cc.gatech.edu/mis2-truefact-kdd2022.html
    KDD 2022 Workshop on Online and Adaptive Recommender Systems
    August 15
    The KDD workshop on online and adaptive recommender systems (OARS) will serve as a platform for publication and discussion of OARS. This workshop will bring together practitioners and researchers from academia and industry to discuss the challenges and approaches to implement OARS algorithms and systems, and improve user experiences by better modeling and responding to user intent.

    Amazon organizer: Tao Ye

    Website: https://oars-workshop.github.io/
    Weighing dynamic availability and consumption for Twitch recommendations
    Edgar Chen, Mark Ally, Eder Santana, Saad Ali
    2022
    Machine learning
    KDD 2022 Workshop on First Content Understanding and Generation for e-Commerce
    August 15 - May 2
    Amazon program committee: Shaunak Mishra, Soumya Roy, Shilpa Ananth, Yashal Kanungo, Indraneil Paul, Bryan Wang, Yang Liu, Jinmiao Fu, Sameer Kanase

    Amazon organizers: Sumit Negi, Rajdeep Banerjee, Manisha Verma, Pooja A

    Website: https://content-generation.github.io/workshop
    Catalog Phrase Grounding (CPG): Grounding of product textual attributes in product images for e-commerce vision-language applications
    Wenyi Wu, Karim Bouyarmane, Ismail Tutar
    2022
    Computer vision
    StFT: Style loss and Fourier transformation for domain gap reduction
    Sarthak Srivastava, Khaleeque Ansari
    2022
    Computer vision
    Machine learning attribution: Inferring item-level impact from slate recommendation in e-commerce
    Shenghe Xu, Yan Zhao, Sameer Kanase, Mitchell Goodman, Saad Khan, Brent Payne, Patricia Grao
    2022
    Machine learning
    KDD 2022 Workshop on Mining and Learning from Time Series – Deep Forecasting: Models, Interpretability, and Applications
    August 15, 8:00 AM - May 15, 5:00 PM EDT
    Amazon workshop organizers: Jun (Luke) Huan, Jan Gasthaus, Yuyang (Bernie) Wang, Hilaf Hasson, Youngsuk Park

    Website: https://kdd-milets.github.io/milets2022/
    Towards robust multivariate time-series forecasting: Adversarial attacks and defense mechanisms
    Youngsuk Park, Trong Nghia Hoang, Hilaf Hasson, Jun Huan
    2022
    Machine learning
    MQTransformer: Multi-horizon forecasts with context dependent attention and optimal bregman volatility
    Kevin C. Chen, Lee Dicker, Carson Eisenach, Dhruv Madeka
    2022
    Machine learning
    MQ-ReTCNN: Multi-horizon time series forecasting with retrieval-augmentation
    Sitan Yang, Carson Eisenach, Dhruv Madeka
    2022
    Machine learning
    Identifying and overcoming transformation bias in forecasting models
    Sushant More
    2022
    Machine learning
    Representation learning using a multi-branch transformer for industrial time series anomaly detection
    Ruichuan Zhang, Fangzhou Cheng, Aparna Pandey
    2022
    Machine learning
    Probabilistic continuous-time whole-graph forecasting
    Zhihan Gao, Hao Wang, Yuyang (Bernie) Wang, Xingjian Shi, Dit-Yan Yeung
    2022
    Machine learning
    KDD 2022 Cup ESCI Challenge for Improving Product Search
    August 15
    In this challenge, we introduce the “Shopping Queries Data Set”, a large dataset of difficult search queries, published with the aim of fostering research in the area of semantic matching of queries and products. The primary objective is to build new ranking strategies and, simultaneously, identify interesting categories of results (i.e., substitutes) that can be used to improve the customer experience when searching for products.

    Amazon chair: Karthik Subbian

    Amazon organizers: Lluis Marquez, Fran Valero, Nikhil Rao, Hugo Zaragoza, Sambaran Bandyopadhyay, Arnab Biswas, Anlu Xing, Chandan K Reddy

    Website: https://www.aicrowd.com/challenges/esci-challenge-for-improving-product-search
    KDD 2022 Workshop on Applied Machine Learning Management
    August 15
    The workshop on Applied Machine Learning Management brings together applied research managers from various fields to share methodologies and case-studies on management of ML teams, products, and projects, achieving business impact with advanced AI-methods.

    Amazon organizer: Elena Sokolova

    Amazon speaker: Shiv Vitaladevuni

    Website: https://wamlm-kdd.github.io/2022
    KDD 2022 Workshop on Anomaly and Novelty Detection, Explanation, and Accommodation (ANDEA)
    August 15
    This workshop will gather researchers and practitioners from diverse communities and knowledge background to promote the development of fundamental theories, effective algorithms, and novel applications of anomaly and novelty detection, characterization, and adaptation.

    Amazon organizer: Anton van den Hengel

    Website: https://sites.google.com/view/andea2022
    One-class predictive autoencoder towards unsupervised anomaly detection on industrial time series
    Hongjing Zhang, Fangzhou Cheng, Aparna Pandey
    2022
    Machine learning
    KDD 2022 Workshop on Artificial Intelligence for Computational Advertising (AdKDD)
    August 15
    AdKDD 2022 is the leading workshop on artificial intelligence for computational advertising in conjunction with KDD 2022.

    Amazon organizer: Kun Liu

    Website: https://www.adkdd.org
    Learning to bid with AuctionGym
    Olivier Jeunen, Sean Murphy, Ben Allison
    2022
    Related: Amazon scientists win best-paper award for ad auction simulator
    Machine learning
    KDD 2022 Workshop on AI-enabled Cybersecurity Analytics and Deployable Defense
    August 15
    This workshop aims to convene academics and practitioners (from industry and government) to share, disseminate, and communicate completed research papers, work in progress, and review articles about AI-enabled cybersecurity analytics and deployable AI-based security defenses.

    Website: https://ai4cyber-kdd.com
    Firenze: Model evaluation using weak signals
    Bhavna Soman, Ali Torkamani, Mike Morais, Jeffrey Bickford, Baris Coskun
    2022
    Security, privacy, and abuse prevention
    KDD 2022 Workshop on Decision Intelligence and Analytics for Online Marketplaces
    August 15
    The goal of this workshop is to offer an opportunity to appreciate the diversity in applications, to draw connections to inform decision optimization across different industries, and to discover new problems that are fundamental to marketplaces of different domains.

    Amazon speaker: Rui Song

    Website: https://sites.google.com/view/kdd22onlinemarketplaces

    Tutorials

    KDD 2022 Tutorial on Hyperbolic Neural Networks: Theory, Architectures and Applications
    August 14, 1:00 PM - 5:00 PM EDT
    The goal of this tutorial is to introduce researchers/practitioners to the fundamentals of hyperbolic geometry and explain the basic implementations of Hyperbolic models. We will describe the hyperbolic variants of the widely used deep learning architectures (such as GNNs, RNNs, and CNNs) and demonstrate their advantages over the standard Euclidean counterparts.

    Amazon organizers: Nurendra Choudhary, Nikhil Rao, Karthik Subbian, Srinivasan H. Sengamedu, and Chandan K. Reddy

    Website: https://www.nurendra.me/hyperbolic-networks-tutorial
    KDD 2022 Tutorial on Graph Neural Networks in Life Sciences
    August 14, 1:00 PM - 5:00 PM EDT
    Graphs are ubiquitous representation in life sciences and medicine. Recent advance in graph machine learning (ML) approaches such as graph neural networks (GNNs) has transformed a diverse set of problems relying on biomedical networks. The objective of this tutorial is twofold. First, it will provide a comprehensive overview of the types of biomedical graphs/networks, the underlying biological and medical problems, and the applications of graph ML algorithms for solving those problems. Second, it will showcase four concrete GNN solutions in life sciences with hands-on experience for the attendees.

    Amazon organizers: Zichen Wang, Vassilis N. Ioannidis, Huzefa Rangwala, Tatsuya Arai, Ryan Brand, Mufei Li, Yohei Nakayama

    Website: https://github.com/dglai/Graph-Neural-Networks-in-Life-Sciences
    KDD 2022 Tutorial on Anomaly Detection for Spatiotemporal Data in Action
    Unknown date
    Amazon authors: Guang Yang, Ninad D Kulkarni, Paavani Dua, Dipika Khullar, Alex Anto Chirayath
    KDD 2022 Tutorial on Multimodal AutoML for Image, Text and Tabular Data
    Unknown date
    Amazon authors: Nick Erickson, Xingjian Shi, James Sharpnack, Alexander Smola
    KDD 2022 Tutorial on Model Monitoring in Practice: Lessons Learned and Open Challenges
    Unknown date
    Amazon author: Pradeep Natarajan
    KDD 2022 Tutorial on Prediction for Enormous and Correlated Output Spaces (PECOS)
    Unknown date
    Amazon authors: Hsiang-Fu Yu, Wei-Cheng Chang, Jyun-Yu Jiang, Wei Li

    Related content

    Work with us

    See more jobs See more jobs
    Intern - Economics, PXT Central Science
    US, WA, Seattle
    The Amazon Economics Team is hiring Economist Interns. We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to work with large and complicated data sets to solve real-world business problems. Some knowledge of econometrics, as well as basic familiarity with Stata, R, or Python is necessary. Experience with SQL, UNIX, Sawtooth, and Spark would be a plus. These are full-time positions at 40 hours per week, with compensation being awarded on an hourly basis. You will learn how to build data sets and perform applied econometric analysis at Internet speed collaborating with economists, data scientists and MBAʼs. These skills will translate well into writing applied chapters in your dissertation and provide you with work experience that may help you with future job market placement. Roughly 85% of interns from previous cohorts have converted to full-time economics employment at Amazon. If you are interested, please send your CV to our mailing list at econ-internship@amazon.com. We are open to hiring candidates to work out of one of the following locations: Seattle, WA, USA
    Applied Scientist
    GB, Cambridge
    Our team undertakes research together with multiple organizations to advance the state-of-the-art in speech technologies. We not only work on giving Alexa, the ground-breaking service that powers Echo, her voice, but we also develop cutting-edge technologies with Amazon Studios, the provider of original content for Prime Video. Do you want to be part of the team developing the latest technology that impacts the customer experience of ground-breaking products? Then come join us and make history. We are looking for a passionate, talented, and inventive Senior Applied Scientist with a background in Machine Learning to help build industry-leading Speech, Language and Video technology. As a Senior Applied Scientist at Amazon you will work with talented peers to develop novel algorithms and modelling techniques to drive the state of the art in speech and vocal arts synthesis. Position Responsibilities: - Participate in the design, development, evaluation, deployment and updating of data-driven models for digital vocal arts applications. - Participate in research activities including the application and evaluation and digital vocal and video arts techniques for novel applications. - Research and implement novel ML and statistical approaches to add value to the business. - Mentor junior engineers and scientists. We are open to hiring candidates to work out of one of the following locations: Cambridge, GBR
    Economist, PXT Central Science
    US, VA, Arlington
    The People eXperience and Technology Central Science Team (PXTCS) uses economics, behavioral science, statistics, and machine learning to proactively identify mechanisms and process improvements which simultaneously improve Amazon and the lives, wellbeing, and the value of work to Amazonians. We are an interdisciplinary team that combines the talents of science and engineering to develop and deliver solutions that measurably achieve this goal. We are looking for economists who are able to apply economic methods to address business problems. The ideal candidate will work with engineers and computer scientists to estimate models and algorithms on large scale data, design pilots and measure their impact, and transform successful prototypes into improved policies and programs at scale. We are looking for creative thinkers who can combine a strong technical economic toolbox with a desire to learn from other disciplines, and who know how to execute and deliver on big ideas as part of an interdisciplinary technical team. Ideal candidates will work in a team setting with individuals from diverse disciplines and backgrounds. They will work with teammates to develop scientific models and conduct the data analysis, modeling, and experimentation that is necessary for estimating and validating models. They will work closely with engineering teams to develop scalable data resources to support rapid insights, and take successful models and findings into production as new products and services. They will be customer-centric and will communicate scientific approaches and findings to business leaders, listening to and incorporate their feedback, and delivering successful scientific solutions. Key job responsibilities Use reduced-form causal analysis and/or structural economic modeling methods to evaluate the impact of policies on employee outcomes, and examine how external labor market and economic conditions impact Amazon's ability to hire and retain talent. A day in the life Work with teammates to apply economic methods to business problems. This might include identifying the appropriate research questions, writing code to implement a DID analysis or estimate a structural model, or writing and presenting a document with findings to business leaders. Our economists also collaborate with partner teams throughout the process, from understanding their challenges, to developing a research agenda that will address those challenges, to help them implement solutions. About the team We are a multidisciplinary team that combines the talents of science and engineering to develop innovative solutions to make Amazon Earth's Best Employer. We are open to hiring candidates to work out of one of the following locations: Arlington, VA, USA
    Actuarial Analyst, Global Risk Management & Claims
    US, WA, Seattle
    We are expanding our Global Risk Management & Claims team and insurance program support for Amazon’s growing risk portfolio. This role will partner with our risk managers to develop pricing models, determine rate adequacy, build underwriting and claims dashboards, estimate reserves, and provide other analytical support for financially prudent decision making. As a member of the Global Risk Management team, this role will provide actuarial support for Amazon’s worldwide operation. Key job responsibilities ● Collaborate with risk management and claims team to identify insurance gaps, propose solutions, and measure impacts insurance brings to the business ● Develop pricing mechanisms for new and existing insurance programs utilizing actuarial skills and training in innovative ways ● Build actuarial forecasts and analyses for businesses under rapid growth, including trend studies, loss distribution analysis, ILF development, and industry benchmarks ● Design actual vs expected and other metrics dashboards to assist decision makings in pricing analysis ● Create processes to monitor loss cost and trends ● Propose and implement loss prevention initiatives with impact on insurance pricing in mind ● Advise underwriting decisions with analysis on driver risk profile ● Support insurance cost budgeting activities ● Collaborate with external vendors and other internal analytics teams to extract insurance insight ● Conduct other ad hoc pricing analyses and risk modeling as needed We are open to hiring candidates to work out of one of the following locations: Arlington, VA, USA | New York, NY, USA | Seattle, WA, USA
    Senior Applied Scientist
    US, NY, New York
    The Amazon SCOT Forecasting team seeks a Senior Applied Scientist to join our team. Our research team conducts research into the theory and application of reinforcement learning. This research is shared in top journals and conferences and has a significant impact on the field. Through our launch of several Deep RL models into production, our work also affects decision making in the real world. Members of our group have varied interests—from the mathematical foundations of reinforcement learning, to language modeling, to maintaining the performance of generative models in the face of copyrights, and more. Recent work has focused on sample efficiency of RL algorithms, treatment effect estimation, and RL agents integrating real-world constraints, as applied in supply chains. Previous publications include: - Linear Reinforcement Learning with Ball Structure Action Space - Meta-Analysis of Randomized Experiments with Applications to Heavy-Tailed Response Data - A Few Expert Queries Suffices for Sample-Efficient RL with Resets and Linear Value Approximation - Deep Inventory Management - What are the Statistical Limits of Offline RL with Linear Function Approximation? Working collaboratively with a group of fellow scientists and engineers, you will identify complex problems and develop solutions in the RL space. We encourage collaboration across teammates and their areas of specialty, leading to creative and ambitious projects with the goal of publication and production. Key job responsibilities - Drive collaborative research and creative problem solving - Constructively critique peer research; mentor junior scientists - Create experiments and prototype implementations of new algorithms and techniques - Collaborate with engineering teams to design and implement software built on these new algorithms - Contribute to progress of the Amazon and broader research communities by producing publications We are open to hiring candidates to work out of one of the following locations: New York, NY, USA
    Data Scientist, Analytics
    US, CA, Virtual Location - California
    If you are interested in this position, please apply on Twitch's Career site https://www.twitch.tv/jobs/en/ About Us: Launched in 2011, Twitch is a global community that comes together each day to create multiplayer entertainment: unique, live, unpredictable experiences created by the interactions of millions. We bring the joy of co-op to everything, from casual gaming to world-class esports to anime marathons, music, and art streams. Twitch also hosts TwitchCon, where we bring everyone together to celebrate and grow their personal interests and passions. We're always live at Twitch. About the Role: As a Data Scientist, Analytics member of the Data Platform - Insights team, you'll provide data analysis and support for platform, service, and operational engineering teams at Twitch, shaping the way success is measured. Defining what questions should be asked and scaling analytics methods and tools to support our growing business. Additionally, you will help support the vision for business analytics, solutions architecture for data related business constructs, as well as tactical execution such as experiment analysis and campaign performance reporting. You are paving the way for high-quality, high-velocity decisions and will report to the Manager, Data Science. For this role, we're looking for an experienced data staff who will oversee data instrumentation, dashboard/report building, metrics reviews, inform team investments, guidance on success/failure metrics and ad-hoc analysis. You will also work with technical and non-technical staff members throughout the company, and your effort will have an impact on hundreds of partners at Twitch You Will: - Work with members of Platforms & Services to guide them towards better decision making from the available data. - Promote data knowledge and insights through managing communications with partners and other teams, collaborate with colleagues to complete data projects and ensure all parties can use the insights to further improve. - Maintain a customer-centric focus while being a domain and product expert through data, develop trust amongst peers, and ensure that the teams and programs have access to data to make decisions - Manage ambiguous problems and adapt tools to answer complicated questions. - Identify the trade-offs between speed and quality of different approaches. - Create analytical frameworks to measure team success by partnering with teams to establish success metrics, create approaches to track the data and troubleshoot errors, measure and evaluate the data to develop a common language for all colleagues to understand these metrics. - Operationalize data processes to provide partners with ad-hoc analysis, automated dashboards, and self-service reporting tools so that everyone gets a good sense of the state of the business Perks: - Medical, Dental, Vision & Disability Insurance - 401(k), Maternity & Parental Leave - Flexible PTO - Commuter Benefits - Amazon Employee Discount - Monthly Contribution & Discounts for Wellness Related Activities & Programs (e.g., gym memberships, off-site massages), -Breakfast, Lunch & Dinner Served Daily - Free Snacks & Beverages We are open to hiring candidates to work out of one of the following locations: Irvine, CA, USA | Seattle, WA, USA | Virtual Location - CA
    Applied Scientist, Middle Mile Transportation
    US, WA, Bellevue
    Have you ever ordered a product on Amazon and when that box with the smile arrived you wondered how it got to you so fast? Have you wondered where it came from and how much it cost Amazon to deliver it to you? Have you also wondered what are different ways that the transportation assets can be used to delight the customer even more. If so, the Amazon transportation Services, Product and Science is for you . We manage the delivery of tens of millions of products every week to Amazon’s customers, achieving on-time delivery in a cost-effective manner. We are looking for an enthusiastic, customer obsessed Applied Scientist with strong scientific thinking, good software and statistics experience, skills to help manage projects and operations, improve metrics, and develop scalable processes and tools. The primary role of an Applied Scientist within Amazon is to address business challenges through building a compelling case, and using data to influence change across the organization. This individual will be given responsibility on their first day to own those business challenges and the autonomy to think strategically and make data driven decisions. Decisions and tools made in this role will have significant impact to the customer experience, as it will have a major impact on how we operate the middle mile network. Ideal candidates will be a high potential, strategic and analytic graduate with a PhD in (Operations Research, Statistics, Engineering, and Supply Chain) ready for challenging opportunities in the core of our world class operations space. Great candidates have a history of operations research, machine learning , and the ability to use data and research to make changes. This role requires robust skills in research and implementation of scalable products and models . This individual will need to be able to work with a team, but also be comfortable making decisions independently, in what is often times an ambiguous environment. Responsibilities may include: - Develop input and assumptions based preexisting models to estimate the costs and savings opportunities associated with varying levels of network growth and operations - Creating metrics to measure business performance, identify root causes and trends, and prescribe action plans - Managing multiple projects simultaneously - Working with technology teams and product managers to develop new tools and systems to support the growth of the business - Communicating with and supporting various internal stakeholders and external audiences We are open to hiring candidates to work out of one of the following locations: Bellevue, WA, USA
    Applied Scientist
    US, CA, Los Angeles
    The Alexa team is looking for a passionate, talented, and inventive Applied Scientist with a strong machine learning background, to help build industry-leading Speech and Language technology. Key job responsibilities As an Applied Scientist with the Alexa team, you will work with talented peers to develop novel algorithms and modeling techniques to advance the state of the art in spoken language understanding. Your work will directly impact our customers in the form of products and services that make use of speech and language technology. You will leverage Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in spoken language understanding. About the team The Alexa team has a mission to push the envelope in Automatic Speech Recognition (ASR), Natural Language Understanding (NLU), and Audio Signal Processing, in order to provide the best-possible experience for our customers. We are open to hiring candidates to work out of one of the following locations: Los Angeles, CA, USA
    Applied Scientist, Intl Seller Growth
    US, WA, Seattle
    Are you fascinated by the power of Natural Language Processing (NLP) and Large Language Models (LLM) to transform the way we interact with technology? Are you passionate about applying advanced machine learning techniques to solve complex challenges in the e-commerce space? If so, Amazon's International Seller Services team has an exciting opportunity for you as an Applied Scientist. At Amazon, we strive to be Earth's most customer-centric company, where customers can find and discover anything they want to buy online. Our International Seller Services team plays a pivotal role in expanding the reach of our marketplace to sellers worldwide, ensuring customers have access to a vast selection of products. As an Applied Scientist, you will join a talented and collaborative team that is dedicated to driving innovation and delivering exceptional experiences for our customers and sellers. You will be part of a global team that is focused on acquiring new merchants from around the world to sell on Amazon’s global marketplaces around the world. The position is based in Seattle but will interact with global leaders and teams in Europe, Japan, China, Australia, and other regions. Join us at the Central Science Team of Amazon's International Seller Services and become part of a global team that is redefining the future of e-commerce. With access to vast amounts of data, cutting-edge technology, and a diverse community of talented individuals, you will have the opportunity to make a meaningful impact on the way sellers engage with our platform and customers worldwide. Together, we will drive innovation, solve complex problems, and shape the future of e-commerce. Please visit https://www.amazon.science for more information Key job responsibilities - Apply your expertise in LLM models to design, develop, and implement scalable machine learning solutions that address complex language-related challenges in the international seller services domain. - Collaborate with cross-functional teams, including software engineers, data scientists, and product managers, to define project requirements, establish success metrics, and deliver high-quality solutions. - Conduct thorough data analysis to gain insights, identify patterns, and drive actionable recommendations that enhance seller performance and customer experiences across various international marketplaces. - Continuously explore and evaluate state-of-the-art NLP techniques and methodologies to improve the accuracy and efficiency of language-related systems. - Communicate complex technical concepts effectively to both technical and non-technical stakeholders, providing clear explanations and guidance on proposed solutions and their potential impact. We are open to hiring candidates to work out of one of the following locations: Seattle, WA, USA
    Applied Scientist II
    US, CA, Palo Alto
    We’re working to improve shopping on Amazon using the conversational capabilities of large language models. We are open to hiring candidates to work out of one of the following locations: Palo Alto, CA, USA
    View from space of a connected network around planet Earth representing the Internet of Things.
    Get more from Amazon Science
    Sign up for our newsletter
    Subscribe
    Amazon.com | Conditions of Use | Privacy | © 1996-2023 Amazon.com, Inc. or its affiliates
    Follow Us