The 10 most viewed publications of 2022

From a look back at Amazon Redshift to personalized complementary product recommendation, these are the most viewed publications authored by Amazon scientists and collaborators in 2022.

  1. In 2013, Amazon Web Services revolutionized the data warehousing industry by launching Amazon Redshift, the first fully managed, petabyte-scale, enterprise-grade cloud data warehouse. Amazon Redshift made it simple and cost-effective to efficiently analyze large volumes of data using existing business intelligence tools.

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    Two authors of Amazon Redshift research paper that will be presented at leading international forum for database researchers reflect on how far the first petabyte scale cloud data warehouse has advanced since it was announced ten years ago.

    This cloud service was a significant leap from the traditional on-premise data warehousing solutions, which were expensive, not elastic, and required significant expertise to tune and operate. Customers embraced Amazon Redshift and it became the fastest growing service in AWS. Today, tens of thousands of customers use Redshift in AWS’s global infrastructure to process exabytes of data daily.

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  2. In this work, we demonstrate that multilingual large-scale sequence-to-sequence (seq2seq) models, pre-trained on a mixture of denoising and Causal Language Modeling (CLM) tasks, are more efficient few-shot learners than decoder-only models on various tasks. In particular, we train a 20 billion parameter multilingual seq2seq model called Alexa Teacher Model (AlexaTM 20B) and show that it achieves state-of-the-art (SOTA) performance on one-shot summarization tasks, outperforming a much larger 540B PaLM decoder model.

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    With an encoder-decoder architecture — rather than decoder only — the Alexa Teacher Model excels other large language models on few-shot tasks such as summarization and machine translation.

    AlexaTM 20B also achieves SOTA in one-shot machine translation, especially for low-resource languages, across almost all language pairs supported by the model (Arabic, English, French, German, Hindi, Italian, Japanese, Marathi, Portuguese, Spanish, Tamil, and Telugu) on Flores-101 dataset. We also show in zero-shot setting, AlexaTM 20B outperforms GPT3 (175B) on SuperGLUE and SQuADv2 datasets and provides SOTA performance on multilingual tasks such as XNLI, XCOPA, Paws-X, and XWinograd. Overall, our results present a compelling case for seq2seq models as a powerful alternative to decoder-only models for Large-scale Language Model (LLM) training.

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  3. Amazon DynamoDB is a NoSQL cloud database service that provides consistent performance at any scale. Hundreds of thousands of customers rely on DynamoDB for its fundamental properties: consistent performance, availability, durability, and a fully managed serverless experience. In 2021, during the 66-hour Amazon Prime Day shopping event, Amazon systems - including Alexa, the Amazon.com sites, and Amazon fulfillment centers, made trillions of API calls to DynamoDB, peaking at 89.2 million requests per second, while experiencing high availability with single-digit millisecond performance.

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    Prioritizing predictability over efficiency, adapting data partitioning to traffic, and continuous verification are a few of the principles that help ensure stability, availability, and efficiency.

    Since the launch of DynamoDB in 2012, its design and implementation have evolved in response to our experiences operating it. The system has successfully dealt with issues related to fairness, traffic imbalance across partitions, monitoring, and automated system operations without impacting availability or performance. Reliability is essential, as even the slightest disruption can significantly impact customers. This paper presents our experience operating DynamoDB at a massive scale and how the architecture continues to evolve to meet the ever-increasing demands of customer workloads.

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  4. We approach instantaneous mapping, converting images to a top-down view of the world, as a translation problem. We show how a novel form of transformer network can be used to map from images and video directly to an overhead map or bird’s-eye-view (BEV) of the world, in a single end-to-end network. We assume a 1-1 correspondence between a vertical scanline in the image, and rays passing through the camera location in an overhead map.

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    Reformulating the mapping problem to take advantage of sequence-to-sequence Transformers improves performance by an average of 15%.

    This lets us formulate map generation from an image as a set of sequence-to-sequence translations. Posing the problem as translation allows the network to use the context of the image when interpreting the role of each pixel. This constrained formulation, based upon a strong physical grounding of the problem, leads to a restricted transformer network that is convolutional in the horizontal direction only. The structure allows us to make efficient use of data when training, and obtains state-of-the-art results for instantaneous mapping of three large-scale datasets, including a 15% and 30% relative gain against existing best performing methods on the nuScenes and Argoverse datasets, respectively.

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  5. A/B tests, also known as online controlled experiments, have been used at scale by data-driven enterprises to guide decisions and test innovative ideas. Meanwhile, non-stationarity, such as the time-of-day effect, can commonly arise in various business metrics. We show that inadequately addressing non-stationarity can cause A/B tests to be statistically inefficient or invalid, leading to wrong conclusions. To address these issues, we develop a new framework that provides appropriate modeling and adequate statistical analysis for non-stationary A/B tests. Without changing the infrastructure for any existing A/B test procedure, we propose a new estimator that views time as a continuous covariate to perform post stratification with a sample-dependent number of stratification levels. We prove central limit theorem in a natural limiting regime under non-stationarity, so that valid large-sample statistical inference is available. We show that the proposed estimator achieves the optimal asymptotic variance among all estimators. When the experiment design phase of an A/B test allows, we propose a new time-grouped randomization approach to make a better balance on treatment and control assignments in presence of time non-stationarity. A brief account of numerical experiments are conducted to illustrate the theoretical analysis.

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  6. We present results from a large-scale experiment on pretraining encoders with non-embedding parameter counts ranging from 700M to 9.3B, their subsequent distillation into smaller models ranging from 17M-170M parameters, and their application to the Natural Language Understanding (NLU) component of a virtual assistant system. Though we train using 70% spoken-form data, our teacher models perform comparably to XLM-R and mT5 when evaluated on the written-form Cross-lingual Natural Language Inference (XNLI) corpus. We perform a second stage of pretraining on our teacher models using in-domain data from our system, improving error rates by 3.86% relative for intent classification and 7.01% relative for slot filling. We find that even a 170M-parameter model distilled from our Stage 2 teacher model has 2.88% better intent classification and 7.69% better slot filling error rates when compared to the 2.3B-parameter teacher trained only on public data (Stage 1), emphasizing the importance of in-domain data for pretraining. When evaluated offline using labeled NLU data, our 17M-parameter Stage 2 distilled model outperforms both XLM-R Base (85M params) and DistillBERT (42M params) by 4.23% to 6.14%, respectively. Finally, we present results from a full virtual assistant experimentation platform, where we find that models trained using our pretraining and distillation pipeline outperform models distilled from 85M parameter teachers by 3.74%-4.91% on an automatic measurement of full-system user dissatisfaction.

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  7. Bayesian optimization (BO) is a widely popular approach for the hyperparameter optimization (HPO) in machine learning. At its core, BO iteratively evaluates promising configurations until a user-defined budget, such as wall-clock time or number of iterations, is exhausted. While the final performance after tuning heavily depends on the provided budget, it is hard to pre-specify an optimal value in advance.

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    Paper presents a criterion for halting the hyperparameter optimization process.

    In this work, we propose an effective and intuitive termination criterion for BO that automatically stops the procedure if it is sufficiently close to the global optimum. Our key insight is that the discrepancy between the true objective (predictive performance on test data) and the computable target (validation performance) suggests stopping once the sub-optimality in optimizing the target is dominated by the statistical estimation error. Across an extensive range of real-world HPO problems and baselines, we show that our termination criterion achieves a better trade-off between the test performance and optimization time. Additionally, we find that overfitting may occur in the context of HPO, which is arguably an overlooked problem in the literature, and show how our termination criterion helps to mitigate this phenomenon on both small and large datasets.

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  8. Online advertising opportunities are sold through auctions, billions of times every day across the web. Advertisers who participate in those auctions need to decide on a bidding strategy: how much they are willing to bid for a given impression opportunity.

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    Paper introduces a unified view of the learning-to-bid problem and presents AuctionGym, a simulation environment that enables reproducible validation of new solutions.

    Deciding on such a strategy is not a straightforward task, because of the interactive and reactive nature of the repeated auction mechanism. Indeed, an advertiser does not observe counterfactual outcomes of bid amounts that were not submitted, and successful advertisers will adapt their own strategies based on bids placed by competitors. These characteristics complicate effective learning and evaluation of bidding strategies based on logged data alone.

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  9. The fundamental challenge of drawing causal inference is that counterfactual outcomes are not fully observed for any unit. Furthermore, in observational studies, treatment assignment is likely to be confounded. Many statistical methods have emerged for causal inference under unconfoundedness conditions given pre-treatment covariates, including: propensity score-based methods, prognostic score-based methods, and doubly robust methods. Unfortunately for applied researchers, there is no ‘one-size-fits-all’ causal method that can perform optimally universally. In practice, causal methods are primarily evaluated quantitatively on handcrafted simulated data. Such datagenerative procedures can be of limited value because they are typically stylized models of reality. They are simplified for tractability and lack the complexities of real-world data. For applied researchers, it is critical to understand how well a method performs for data at hand. Our work introduces a deep generative model-based framework, Credence, to validate causal inference methods. The framework’s novelty stems from its ability to generate synthetic data anchored at the empirical distribution for the observed sample, and therefore virtually indistinguishable from the latter. The approach allows the user to specify ground truth for the form and magnitude of causal effects and confounding bias as functions of covariates. Thus simulated data sets are used to evaluate the potential performance of various causal estimation methods when applied to data similar to the observed sample. We demonstrate Credence’s ability to accurately assess the relative performance of causal estimation techniques in an extensive simulation study and two real-world data applications from Lalonde and Project STAR studies.

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  10. Complementary product recommendation aims at providing product suggestions that are often bought together to serve a joint demand. Existing work mainly focuses on modeling product relationships at a population level, but does not consider personalized preferences of different customers. In this paper, we propose a framework for personalized complementary product recommendation capable of recommending products that fit the demand and preferences of the customers. Specifically, we model product relations and user preferences with a graph attention network and a sequential behavior transformer, respectively. The two networks are cast together through personalized re-ranking and contrastive learning, in which the user and product embedding are learned jointly in an end-to-end fashion. The system recognizes different customer interests by learning from their purchase history and the correlations among customers and products. Experimental results demonstrate that our model benefits from learning personalized information and outperforms non-personalized methods on real production data.

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US, WA, Seattle
Do you want to join an innovative team of scientists who use machine learning to help Amazon provide the best experience to our Selling Partners by automatically understanding and addressing their challenges, needs and opportunities? Do you want to build advanced algorithmic systems that are powered by state-of-art ML, such as Natural Language Processing, Large Language Models, Deep Learning, Computer Vision and Causal Modeling, to seamlessly engage with Sellers? Are you excited by the prospect of analyzing and modeling terabytes of data and creating cutting edge algorithms to solve real world problems? Do you like to build end-to-end business solutions and directly impact the profitability of the company and experience of our customers? Do you like to innovate and simplify? If yes, then you may be a great fit to join the Selling Partner Experience Science team. Key job responsibilities - Use statistical and machine learning techniques to create the next generation of the tools that empower Amazon's Selling Partners to succeed. - Design, develop and deploy highly innovative models to interact with Sellers and delight them with solutions. - Work closely with teams of scientists and software engineers to drive real-time model implementations and deliver novel and highly impactful features. - Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation. - Research and implement novel machine learning and statistical approaches. - Participate in strategic initiatives to employ the most recent advances in ML in a fast-paced, experimental environment. About the team Selling Partner Experience Science is a growing team of scientists, engineers and product leaders engaged in the research and development of the next generation of ML-driven technology to empower Amazon's Selling Partners to succeed. We draw from many science domains, from Natural Language Processing to Computer Vision to Optimization to Economics, to create solutions that seamlessly and automatically engage with Sellers, solve their problems, and help them grow. Focused on collaboration, innovation and strategic impact, we work closely with other science and technology teams, product and operations organizations, and with senior leadership, to transform the Selling Partner experience. We are open to hiring candidates to work out of one of the following locations: Denver, CO, USA | Seattle, WA, USA
US, WA, Seattle
Amazon is investing heavily in building a world class advertising business and developing a collection of self-service performance advertising products that drive discovery and sales. Our products are strategically important to our Retail and Marketplace businesses for 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. We are highly motivated, collaborative and fun-loving with an entrepreneurial spirit and 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. Key job responsibilities Search Supply and Experiences, within Sponsored Products, is seeking a Senior Data Scientist to join a fast growing team with the mandate of creating new ads experience that elevates the shopping experience for our hundreds of millions customers worldwide. We are looking for a top analytical mind capable of understanding our complex ecosystem of advertisers participating in a pay-per-click model– and leveraging this knowledge to help turn the flywheel of the business. As a Senior Data Scientist on this team you will: - Lead Data Science solutions from beginning to end. - Deliver with independence on challenging large-scale problems with ambiguity. - Manage and drive the technical and analytical aspects of Advertiser segmentation; continually advance approach and methods. - Write code (Python, R, Scala, etc.) to analyze data and build statistical models to solve specific business problems - Retrieve, synthesize, and present critical data in a format that is immediately useful to answering specific questions or improving system performance. - Analyze historical data to identify trends and support decision making. - Improve upon existing methodologies by developing new data sources, testing model enhancements, and fine-tuning model parameters. - Provide requirements to develop analytic capabilities, platforms, and pipelines. - Apply statistical and machine learning knowledge to specific business problems and data. - Formalize assumptions about how our systems should work, create statistical definitions of outliers, and develop methods to systematically identify outliers. Work out why such examples are outliers and define if any actions needed. - Given anecdotes about anomalies or generate automatic scripts to define anomalies, deep dive to explain why they happen, and identify fixes. - Build decision-making models and propose solution for the business problem you defined - Conduct written and verbal presentation to share insights and recommendations to audiences of varying levels of technical sophistication. - Write code (python or another object-oriented language) for data analyzing and modeling algorithms. A day in the life The Senior Data Scientist will have the opportunity to use one of the world's largest eCommerce and advertising data sets to influence the evolution of our products. This role requires an individual with excellent business, communication, and technical skills, enabling collaboration with various functions, including product managers, software engineers, economists and data scientists, as well as senior leadership. This role will create and enhance performance monitoring reports to find insights that product and business team should focus on. The successful candidate will be a self-starter comfortable with ambiguity, with strong attention to detail, and with an ability to work in a fast-paced, high-energy and ever-changing environment. The drive and capability to shape the direction is a must. This role will influence the direction of the business by leveraging our data to deliver insights that drive decisions and actions. The role will involve translating broad business problems into specific analytics projects, conducting deep quantitative analyses, and communicating results effectively. The role will help the organization identify, evaluate, and evangelize new techniques and tools to continue to improve our ability to deliver value to Amazon’s customers. About the team We are a customer-obsessed team of engineers, technologists, product leaders, and scientists. We are focused on continuous exploration of contexts and creatives where advertising delivers value to customers and advertisers. We specifically work on new ads experiences globally with the goal of helping shoppers make the most informed purchase decision. We obsess about our customers and we are continuously innovating on their behalf to enrich their shopping experience on Amazon We are open to hiring candidates to work out of one of the following locations: Seattle, WA, USA
US, CA, Pasadena
The Amazon Web Services (AWS) Center for Quantum Computing (CQC) is a multi-disciplinary team of scientists, engineers, and technicians, on a mission to develop a fault-tolerant quantum computer. We are looking to hire an Applied Scientist to work on the embedded software for our control system. The position is on-site at our lab, located on the Caltech campus in Pasadena, CA. The ideal candidate will be able to translate high-level requirements (e.g. latency, bandwidth, architecture) into software/firmware implementations (e.g. low-level device drivers, kernel modules, Python APIs) compatible with our FPGA-based control systems. This requires someone who (1) has a strong desire to work within a team of scientists and engineers, and (2) demonstrates ownership in initiating and driving projects to completion. Key job responsibilities - Develop embedded software in C, C++ or Rust for high-performance real-time tasks. - Develop Linux and/or real-time operating system (RTOS) features required to operate control system. - Develop FPGA gateware that drives domain-specific functions of our control hardware. - Develop user-space API that exposes low-level features, preferably in Python. - Develop, test, and optimize control system features on bench-top and in real-world conditions. - Own the stability of control system software and firmware. We are looking for candidates with strong engineering principles, resourcefulness and a bias for action, superior problem-solving and excellent communication skills. Working effectively within a team environment is essential. You will have the opportunity to work on new ideas and stay abreast of the field of experimental quantum computation. A day in the life The lifetime of your projects will likely begin with a lot of discussion and negotiation with our scientists and engineers to translate their software and hardware feature requests into design proposals that demonstrate sensible trade-offs between complexity and delivery. Once a design proposal has been accepted, you will implement it in a logical and maintainable manner. You will also be encouraged to take ownership over the stability and quality of the software and hardware stack by identifying, proposing, and implementing features that will accelerate our realization of quantum computing technologies. You will be joining the Control & Calibration Software team within the AWS Center of Quantum Computing. Our team is comprised of scientists and engineers who are building scalable software that enables quantum computing technologies. About the team AWS Utility Computing (UC) provides product innovations — from foundational services such as Amazon’s Simple Storage Service (S3) and Amazon Elastic Compute Cloud (EC2), to consistently released new product innovations that continue to set AWS’s services and features apart in the industry. As a member of the UC organization, you’ll support the development and management of Compute, Database, Storage, Internet of Things (Iot), Platform, and Productivity Apps services in AWS, including support for customers who require specialized security solutions for their cloud services. 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 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 in the cloud. Hybrid Work We value innovation and recognize this sometimes requires uninterrupted time to focus on a build. We also value in-person collaboration and time spent face-to-face. Our team affords employees options to work in the office every day or in a flexible, hybrid work model near one of our U.S. Amazon offices. We are open to hiring candidates to work out of one of the following locations: Pasadena, CA, USA
ES, M, Madrid
Amazon's International Technology org in EU (EU INTech) is creating new ways for Amazon customers discovering Amazon catalog through new and innovative Customer experiences. Our vision is to provide the most relevant content and CX for their shopping mission. We are responsible for building the software and machine learning models to surface high quality and relevant content to the Amazon customers worldwide across the site. The team, mainly located in Madrid Technical Hub, London and Luxembourg, comprises Software Developer and ML Engineers, Applied Scientists, Product Managers, Technical Product Managers and UX Designers who are experts on several areas of ranking, computer vision, recommendations systems, Search as well as CX. Are you interested on how the experiences that fuel Catalog and Search are built to scale to customers WW? Are interesting on how we use state of the art AI to generate and provide the most relevant content? Key job responsibilities We are looking for Applied Scientists who are passionate to solve highly ambiguous and challenging problems at global scale. You will be responsible for major science challenges for our team, including working with text to image and image to text state of the art models to scale to enable new Customer Experiences WW. You will design, develop, deliver and support a variety of models in collaboration with a variety of roles and partner teams around the world. You will influence scientific direction and best practices and maintain quality on team deliverables. We are open to hiring candidates to work out of one of the following locations: Madrid, M, ESP
US, WA, Seattle
Alexa is the Amazon cloud service that powers Echo, the groundbreaking Amazon device designed around your voice. We believe voice is the most natural user interface for interacting with technology across many domains; we are inventing the future. Alexa Audio is responsible for fulfilling customers requests for all types of audio content (Music, Radio, Podcasts, Books, custom sounds) across all Alexa enabled devices. This covers a broad set of experiences including search, browse, recommendations, playback, and devices grouping and controls. We are seeking a talented, self-directed Applied Scientists who would come up with state of the art semantic search and recommendation techniques that work with both voice and visual interfaces. This is a unique opportunity where you will be working on latest technologies including LLMs, and also see it impact customer's lives in meaningful ways. Responsibilities - Apply advance state-of-the-art artificial intelligence techniques and develop algorithms in areas of personalization, voice based dialogue systems and natural language information retrieval. - Design scientifically sound online experiments and offline simulations to study and improve products. - Work closely with talented engineers to create scalable models and put them to production. - Perform statistical analyses on large data sets, identify problems, and propose solutions. - Work with partner science teams to identify collaboration opportunities. Work hard. Have fun. Make history. We are open to hiring candidates to work out of one of the following locations: Bellevue, WA, USA | Seattle, WA, USA | Sunnyvale, CA, USA
GB, London
Amazon Advertising is looking for an Applied Scientist to join its initiative that powers Amazon’s contextual advertising products. Advertising at Amazon is a fast-growing multi-billion dollar business that spans across desktop, mobile and connected devices; encompasses ads on Amazon and a vast network of hundreds of thousands of third party publishers; and extends across US, EU and an increasing number of international geographies.The Supply Quality organization has the charter to solve optimization problems for ad-programs in Amazon and ensure high-quality ad-impressions. We develop advanced algorithms and infrastructure systems to optimize performance for our advertisers and publishers. We are focused on solving a wide variety of problems in computational advertising like Contextual data processing and classification, traffic quality prediction (robot and fraud detection), Security forensics and research, Viewability prediction, Brand Safety and experimentation. Our team includes experts in the areas of distributed computing, machine learning, statistics, optimization, text mining, information theory and big data systems. We are looking for a dynamic, innovative and accomplished Applied Scientist to work on machine learning and data science initiatives for contextual data processing and classification that power our contextual advertising solutions. Are you excited by the prospect of analyzing terabytes of data and leveraging state-of-the-art data science and machine learning techniques to solve real world problems? Do you like to own business problems/metrics of high ambiguity where yo get to define the path forward for success of a new initiative? As an applied scientist, you will invent ML based solutions to power our contextual classification technology. As this is a new initiative, you will get an opportunity to act as a thought leader, work backwards from the customer needs, dive deep into data to understand the issues, conceptualize and build algorithms and collaborate with multiple cross-functional teams. Key job responsibilities * Design, prototype and test many possible hypotheses in a high-ambiguity environment, making use of both analysis and business judgment. * Collaborate with software engineering teams to integrate successful experiments into large-scale, highly complex Amazon production systems. * Promote the culture of experimentation and applied science at Amazon. * Demonstrated ability to meet deadlines while managing multiple projects. * Excellent communication and presentation skills working with multiple peer groups and different levels of management * Influence and continuously improve a sustainable team culture that exemplifies Amazon’s leadership principles. We are open to hiring candidates to work out of one of the following locations: London, GBR
US, WA, Bellevue
The Planning and Execution team (PLEX) is seeking a Research Scientist to build & improve mathematical optimization techniques and algorithms to support planning and execution activities throughout North America. PLEX is comprised of high-powered dynamic teams, which are shaping network execution through the development and application of innovative labor & flow planning mechanisms. Our goal is to improve and enhance the Amazon Fulfillment network to ultimately drive the best customer experience in a reliable and cost-efficient manner that is truly world-class. As part of the PLEX organization, you’ll partner closely with other scientists, engineers, and product teams in a collegial environment to build optimization strategies that will influence the performance of all North America Amazon Fulfillment networks. You will develop scientific models and perform complex mathematical research to accurately solve labor and flow planning problems, enhance automation, and provide value-added research to the business. You will continually iterate and identify new modeling and research opportunities to implement science into customer fulfillment planning processes. We are looking for a passionate scientist with a commitment to innovation & teamwork. Successful candidates will have a deep knowledge of optimization techniques and ML methods to tackle complex science problems. You will have the communication skills necessary to impact and influence leadership & partner teams through technical writings, presentations and discussions. You will learn a lot, grow, and have fun in the process! Innovation Opportunities & Career Growth Our business grows fast and we want our employees growing with it too. We provide constant opportunities for growth in our team through regular training, talent development, mentoring, and mechanisms conducive to incubating ideas from the bottom up to showcase your innovations. Inclusive Team Culture Here at Amazon, we promote an inclusive and engaging environment. We understand the strength that unique experiences bring to the team and value it. In our team, we uphold that all individuals should feel included, respected, and developed. Flexibility It's not the hours that you put into work matters, rather it's the quality of work that you put in. We provide flexibility and support to help you find a balance between your work and personal lives. This position will be based in Austin, TX We are open to hiring candidates to work out of one of the following locations: - Austin, TX - Bellevue, WA - Nashville, TN Key job responsibilities - Create & improve mathematical optimization techniques & ML models for labor & flow planning - Lead & partner with research, applied, and data science teams to improve accuracy of existing technology solutions and provide data driven recommendations for strategic model implementations - Identify and thoroughly research external and previously non-considered factors to implement with advanced mathematics - Simplify the scientific decisions by navigating through the technology complexities, explaining them in plain customer and business context to our partners & customers. We are open to hiring candidates to work out of one of the following locations: Austin, TX, USA | Bellevue, WA, USA | Nashville, TN, USA
US, WA, Seattle
We are building GenAI based shopping assistant for Amazon. We reimage Amazon Search with an interactive conversational experience that helps you find answers to product questions, perform product comparisons, receive personalized product suggestions, and so much more, to easily find the perfect product for your needs. We’re looking for the best and brightest across Amazon to help us realize and deliver this vision to our customers right away. This will be a once in a generation transformation for Search, just like the Mosaic browser made the Internet easier to engage with three decades ago. If you missed the 90s—WWW, Mosaic, and the founding of Amazon and Google—you don’t want to miss this opportunity. We are open to hiring candidates to work out of one of the following locations: Seattle, WA, USA
US, NY, New York
AWS Utility Computing (UC) provides product innovations — from foundational services such as Amazon’s Simple Storage Service (S3) and Amazon Elastic Compute Cloud (EC2), to consistently released new product innovations that continue to set AWS’s services and features apart in the industry. As a member of the UC organization, you’ll support the development and management of Compute, Database, Storage, Internet of Things (Iot), Platform, and Productivity Apps services in AWS. Within AWS UC, Amazon Dedicated Cloud (ADC) roles engage with AWS customers who require specialized security solutions for their cloud services. Amazon AI is looking for world class scientists and engineers to join its AWS AI Labs to develop groundbreaking generative AI technologies in Amazon Q. Q is an interactive, AI-powered assistant that touches all aspects of builder and developer experience. You will be part of the Q Code Analysis team that works at the intersection of code analysis, logical reasoning and machine learning to build and enhance capabilities, safety and security of AI-powered developer tools in Amazon Q. You will invent, implement, and deploy state-of-the-art algorithms and systems, and be at the heart of a growing and exciting focus area for AWS. Your work will directly impact millions of our customers in the form of products and services that are based on large language models, retrieval-augmented generation, code analysis, responsible AI, and a lot more. You will make breakthroughs that challenge the limits of code analysis, machine learning and AI while collaborating with academics and interacting directly with customers to bring new research rapidly to production. A day in the life 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 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 in the cloud. EEO/Accommodations AWS is committed to a diverse and inclusive workplace to deliver the best results for our customers. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status; we celebrate the diverse ways we work. For individuals with disabilities who would like to request an accommodation, please let us know and we will connect you to our accommodation team. You may also reach them directly by visiting please https://www.amazon.jobs/en/disability/us. Hybrid Work We value innovation and recognize this sometimes requires uninterrupted time to focus on a build. We also value in-person collaboration and time spent face-to-face. Our team affords employees options to work in the office every day or in a flexible, hybrid work model near one of our [insert req country location here] Amazon offices. About the team The Amazon Web Services (AWS) Next Gen DevX (NGDE) team uses generative AI and foundation models to reimagine the experience of all builders on AWS. From the IDE to web-based tools and services, AI will help engineers work on large and small applications. We explore new technologies and find creative solutions. Curiosity and an explorative mindset can find a place here to impact the life of engineers around the world. If you are excited about this space and want to enlighten your peers with new capabilities, this is the team for you. We are open to hiring candidates to work out of one of the following locations: New York, NY, USA
DE, Berlin
The Amazon Artificial General Intelligence (AGI) team is looking for a passionate, highly skilled and inventive Senior Applied Scientist with strong machine learning background to lead the development and implementation of state-of-the-art ML systems for building large-scale, high-quality conversational assistant systems. Key job responsibilities - Use deep learning, ML and NLP techniques to create scalable solutions for creation and development of language model centric solutions for building personalized assistant systems based on a rich set of structured and unstructured contextual signals - Innovate new methods for contextual knowledge extraction and information representation, using language models in combination with other learning techniques, that allows effective grounding in context providers when considering memory, cpu, latency and quality - Collaborate with cross-functional teams of engineers, product managers, and scientists to identify and solve complex problems in personal knowledge aggregation, processing and verification - Design and execute experiments to evaluate the performance of different algorithms and models, and iterate quickly to improve results - Think Big about the arc of development of conversational assistant system personalization over a multi-year horizon, and identify new opportunities to apply these technologies to solve real-world problems - Communicate results and insights to both technical and non-technical audiences, including through presentations and written reports - Mentor and guide junior scientists and engineers, and contribute to the overall growth and development of the team A day in the life As a Senior Applied Scientist, you will play a critical role in driving the development of personalization techniques enabling conversational systems, in particular those based on large language models, to be tailored to customer needs. You will handle Amazon-scale use cases with significant impact on our customers' experiences. We are open to hiring candidates to work out of one of the following locations: Berlin, DEU