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Publications

Amazon is a great place to practice science and have real business impact, but that’s only one part of the story. Our scientists continue to publish, teach, and engage with the worldwide research community.
104 results found
  • Srujan Meesala, Jash Banker, Steven Wood, Alp Sipahigil, David Lake, Piero Chiappina, Andrew Beyer, Matthew Shaw, Oskar Painter
    CLEO 2021
    2021
    Decoherence and noise from optical absorption in superconducting circuits hinder development of microwave to optical quantum transducers. Addressing these issues, we fabricate niobium-based resonators and qubits, and study them under laser illumination at milliKelvin temperatures.
  • Matthew Ware, Guilhem Ribeill, Diego Ristè, Colm A. Ryan, Blake R. Johnson, Marcus P. da Silva
    Physical Review A
    2021
    The promise of quantum computing with imperfect qubits relies on the ability of a quantum computing system to scale cheaply through error correction and fault tolerance. While fault tolerance requires relatively mild assumptions about the nature of qubit errors, the overhead associated with coherent and non-Markovian errors can be orders of magnitude larger than the overhead associated with purely stochastic
  • Chiao-Hsuan Wang, Kyungjoo Noh, José Lebreuilly, S.M. Girvin, Liang Jiang
    Physical Review Applied
    2021
    Cavity resonators are promising resources for quantum technology, while native nonlinear interactions for cavities are typically too weak to provide the level of quantum control required to deliver complex targeted operations. Here we investigate a scheme to engineer a target Hamiltonian for photonic cavities using ancilla qubits. By off resonantly driving dispersively coupled ancilla qubits, we develop
  • James R. Seddon, Bartosz Regula, Hakop Pashayan, Yingkai Ouyang, Earl Campbell
    PRX Quantum
    2021
    Consumption of magic states promotes the stabilizer model of computation to universal quantum computation. Here, we propose three different classical algorithms for simulating such universal quantum circuits, and characterize them by establishing precise connections with a family of magic monotones. Our first simulator introduces a new class of quasiprobability distributions and connects its runtime to
  • Patrick Hayden, Sepehr Nezami, Sandu Popescu, Grant Salton
    PRX Quantum
    2021
    The existence of quantum error-correcting codes is one of the most counterintuitive and potentially technologically important discoveries of quantum-information theory. In this paper, we study a problem called “covariant quantum error correction”, in which the encoding is required to be group covariant. This problem is intimately tied to fault-tolerant quantum computation and the well-known Eastin-Knill
  • Eunjong Kim, Xueyue Zhang, Vinicius S. Ferreira, Jash Banker, Joseph K. Iverson, Alp Sipahigil, Miguel Bello, Alejandro González-Tudela, Mohammad Mirhosseini, Oskar Painter
    Physical Review X
    2021
    While designing the energy-momentum relation of photons is key to many linear, nonlinear, and quantum optical phenomena, a new set of light-matter properties may be realized by employing the topology of the photonic bath itself. In this work we experimentally investigate the properties of superconducting qubits coupled to a metamaterial waveguide based on a photonic analog of the Su-Schrieffer-Heeger model
  • Zijun Chen , Kevin J. Satzinger, Juan Atalaya, Alexander Alexandrov, Andrew Dunsworth , Daniel Sank , Chris Quintana , Matt McEwen, Rami Barends, Paul V. Klimov, Sabrina Hong , Cody Jones , Andre Petukhov, Dvir Kafri, Sean Demura, Brian Burkett, Craig Gidney, Austin G. Fowler, Alexandru Paler, Harald Putterman, Igor Aleiner, Frank Arute , Kunal Arya , Ryan Babbush , Joseph C. Bardin, Andreas Bengtsson, Alexandre Bourassa, Michael Broughton, Bob B. Buckley , David A. Buell, Nicholas Bushnell, Benjamin Chiaro, Roberto Collins, William Courtney, Alan R. Derk, Daniel Eppens, Catherine Erickson, E. Farhi, Brooks Foxen, Marissa Giustina, Ami Greene, Jonathan Gross, Matthew P. Harrigan, Sean D. Harrington, Jeremy Hilton, Alan Ho , Trent Huang, William J. Huggins, L. B. Ioffe, Sergei V. Isakov, Evan Jeffrey, Zhang Jiang, Kostyantyn Kechedzhi, Seon Kim, Alexei Kitaev, Fedor Kostritsa, David Landhuis, Pavel Laptev, Erik Lucero, Orion Martin, Jarrod R. McClean, Trevor McCourt, Xiao Mi, Kevin C. Miao, Masoud Mohseni, Shirin Montazeri, Wojciech Mruczkiewicz, Josh Mutus, Ofer Naaman, Matthew Neeley, Charles Neill, Michael Newman, Murphy Yuezhen Niu, Thomas E. O’Brien, Alex Opremcak, Eric Ostby, Bálint Pató, Nicholas Redd, Pedram Roushan, Nicholas C. Rubin, Vladimir Shvarts, Doug Strain, Marco Szalay, Matthew D. Trevithick, Benjamin Villalonga, Theodore White, Z. Jamie Yao , Ping Yeh, Juhwan Yoo, Adam Zalcman, Hartmut Neven, Sergio Boixo, Vadim Smelyanskiy, Yu Chen, Anthony Megrant, Julian Kelly
    Nature
    2021
    Realizing the potential of quantum computing requires sufficiently low logical error rates(1). Many applications call for error rates as low as 10⁻¹⁵ (refs. 2,3,4,5,6,7,8,9), but state-of-the-art quantum platforms typically have physical error rates near 10⁻³ (refs. 10,11,12,13,14). Quantum error correction(15,16,17) promises to bridge this divide by distributing quantum logical information across many
  • Jasminder S. Sidhu, Yingkai Ouyang, Earl Campbell, Pieter Kok
    Physical Review X
    2021
    The estimation of multiple parameters in quantum metrology is important for a vast array of applications in quantum information processing. However, the unattainability of fundamental precision bounds for incompatible observables greatly diminishes the applicability of estimation theory in many practical implementations. The Holevo Cramér-Rao bound (HCRB) provides the most fundamental, simultaneously attainable
  • Andrew Patterson, Hongxiang Chen, Leonard Wossnig, Simone Severini, Dan Browne, Ivan Rungger
    Physical Review Research
    2021
    Near-term quantum computers are noisy, and therefore must run algorithms with a low circuit depth and qubit count. Here we investigate how noise affects a quantum neural network (QNN) for state discrimination, which is applicable on near-term quantum devices as it fulfils the above criteria. We find that for the required gradient calculation on a noisy device a quantum circuit with a large number of parameters
  • Bart McGuyer, Qi Tang
    APS Physical Review Applied
    2021
    The moiré effect provides an interpretation for the steering of antennas that form beams through internal spatial interferences. We make an explicit connection between such antennas and the moiré effect, and use it to model six planar antennas that steer by scaling, rotating, or translating operations. Three of the antennas illustrate how to use moiré patterns to generate antenna designs.
  • Alex Dalzell, Nicholas Hunter-Jones, Fernando Brandão
    arXiv
    2021
    We study the distribution over measurement outcomes of noisy random quantum circuits in the low-fidelity regime. We show that, for local noise that is sufficiently weak and unital, correlations (measured by the linear cross-entropy benchmark) between the output distribution p_(noisy) of a generic noisy circuit instance and the output distribution pideal of the corresponding noiseless instance shrink exponentially
  • Stefanie Czischek, Giacomo Torlai, Sayonee Ray, Rajibul Islam, Roger G. Melko
    Physical Review A
    2021
    The rise of programmable quantum devices has motivated the exploration of circuit models which could realize novel physics. A promising candidate is a class of hybrid circuits, where entangling unitary dynamics compete with disentangling measurements. Novel phase transitions between different entanglement regimes have been identified in their dynamical states, with universal properties hinting at unexplored
  • Chi-Fang Chen, Fernando Brandão
    arXiv
    2021
    The Eigenstate Thermalization Hypothesis (ETH) has played a major role in explaining thermodynamic phenomena in quantum systems. However, so far, no connection has been known between ETH and the timescale of thermalization. In this paper, we rigorously show that ETH indeed implies fast thermalization to the global Gibbs state. We show fast convergence for two models of thermalization. In the first, the
  • John Napp, Rolando La Placa, Alex Dalzell, Fernando Brandão, Aram Harrow
    arXiv
    2021
    Random quantum circuits are commonly viewed as hard to simulate classically. In some regimes this has been formally conjectured — in the context of deep 2D circuits, this is the basis for Google’s recent announcement of “quantum computational supremacy” — and there had been no evidence against the more general possibility that for circuits with uniformly random gates, approximate simulation of typical instances
  • Yuan Su, Hsin-Yuan (Robert) Huang, Earl Campbell
    QIP 2021
    2021
    We consider simulating quantum systems on digital quantum computers. We show that the performance of quantum simulation can be improved by simultaneously exploiting the commutativity of Hamiltonian, the sparsity of interactions, and the prior knowledge of initial state. We achieve this using Trotterization for a class of correlated electrons that encompasses various physical systems, including the plane-wave-basis

Latest news

US, MA, North Reading
Working at Amazon Robotics Are you inspired by invention? Is problem solving through teamwork in your DNA? Do you like the idea of seeing how your work impacts the bigger picture? Answer yes to any of these and you’ll fit right in here at Amazon Robotics. We are a smart, collaborative team of doers that work passionately to apply cutting-edge advances in robotics and software to solve real-world challenges that will transform our customers’ experiences in ways we can’t even imagine yet. We invent new improvements every day. We are Amazon Robotics and we will give you the tools and support you need to invent with us in ways that are rewarding, fulfilling and fun. Position Overview The Amazon Robotics (AR) Software Research and Science team builds and runs simulation experiments and delivers analyses that are central to understanding the performance of the entire AR system. This includes operational and software scaling characteristics, bottlenecks, and robustness to “chaos monkey” stresses -- we inform critical engineering and business decisions about Amazon’s approach to robotic fulfillment. We are seeking an enthusiastic Data Scientist to design and implement state-of-the-art solutions for never-before-solved problems. The DS will collaborate closely with other research and robotics experts to design and run experiments, research new algorithms, and find new ways to improve Amazon Robotics analytics to optimize the Customer experience. They will partner with technology and product leaders to solve business problems using scientific approaches. They will build new tools and invent business insights that surprise and delight our customers. They will work to quantify system performance at scale, and to expand the breadth and depth of our analysis to increase the ability of software components and warehouse processes. They will work to evolve our library of key performance indicators and construct experiments that efficiently root cause emergent behaviors. They will engage with software development teams and warehouse design engineers to drive the evolution of the AR system, as well as the simulation engine that supports our work. Inclusive Team Culture Here at Amazon, we embrace our differences. We are committed to furthering our culture of inclusion. We have 12 affinity groups (employee resource groups) with more than 87,000 employees across hundreds of chapters around the world. We have innovative benefit offerings and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon’s culture of inclusion is reinforced within our 14 Leadership Principles, which reminds team members to seek diverse perspectives, learn and be curious, and earn trust. Flexibility It isn’t about which hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We offer flexibility and encourage you to find your own balance between your work and personal lives. Mentorship & Career Growth We care about your career growth too. Whether your goals are to explore new technologies, take on bigger opportunities, or get to the next level, we'll help you get there. Our business is growing fast and our people will grow with it. A day in the life Amazon offers a full range of benefits that support you and eligible family members, including domestic partners and their children. Benefits can vary by location, the number of regularly scheduled hours you work, length of employment, and job status such as seasonal or temporary employment. The benefits that generally apply to regular, full-time employees include: 1. Medical, Dental, and Vision Coverage 2. Maternity and Parental Leave Options 3. Paid Time Off (PTO) 4. 401(k) Plan If you are not sure that every qualification on the list above describes you exactly, we'd still love to hear from you! At Amazon, we value people with unique backgrounds, experiences, and skillsets. If you’re passionate about this role and want to make an impact on a global scale, please apply! A day in the life Amazon offers a full range of benefits that support you and eligible family members, including domestic partners and their children. Benefits can vary by location, the number of regularly scheduled hours you work, length of employment, and job status such as seasonal or temporary employment. The benefits that generally apply to regular, full-time employees include: 1. Medical, Dental, and Vision Coverage 2. Maternity and Parental Leave Options 3. Paid Time Off (PTO) 4. 401(k) Plan If you are not sure that every qualification on the list above describes you exactly, we'd still love to hear from you! At Amazon, we value people with unique backgrounds, experiences, and skillsets. If you’re passionate about this role and want to make an impact on a global scale, please apply! We are open to hiring candidates to work out of one of the following locations: North Reading, MA, USA
DE, Berlin
AWS AI is looking for passionate, talented, and inventive Applied Scientists with a strong machine learning background to help build industry-leading Conversational AI Systems. Our mission is to provide a delightful experience to Amazon’s customers by pushing the envelope in Natural Language Understanding (NLU), Dialog Systems including Generative AI with Large Language Models (LLMs) and Applied Machine Learning (ML). As part of our AI team in Amazon AWS, you will work alongside internationally recognized experts to develop novel algorithms and modeling techniques to advance the state-of-the-art in human language technology. Your work will directly impact millions of our customers in the form of products and services that make use language technology. You will gain hands on experience with Amazon’s heterogeneous text, structured data sources, and large-scale computing resources to accelerate advances in language understanding. We are hiring in all areas of human language technology and code generation. We are open to hiring candidates to work out of one of the following locations: Berlin, DEU
CN, Shanghai
亚马逊云科技上海人工智能实验室OpenSearch 研发团队正在招募应用科学实习生-多模态检索与生成方向实习生。OpenSearch是一个开源的搜索和数据分析套件, 它旨在为数据密集型应用构建解决方案,内置高性能、开发者友好的工具,并集成了强大的机器学习、数据处理功能,可以为客户提供灵活的数据探索、丰富和可视化功能,帮助客户从复杂的数据中发现有价值的信息。OpenSearch是现有AWS托管服务(AWS OpenSearch)的基础,OpenSearch核心团队负责维护OpenSearch代码库,他们的目标是使OpenSearch安全、高效、可扩展、可扩展并永远开源。 点击下方链接查看申请手册获得更多信息: https://amazonexteu.qualtrics.com/CP/File.php?F=F_55YI0e7rNdeoB6e Key job responsibilities 在这个实习期间,你将有机会: 1. 研究最新的搜索相关性人工智能算法。 2. 探索大模型技术在数据分析与可视化上的应用。 3. 了解主流搜索引擎Lucene的原理和应用。深入了解前沿自然语言处理技术和底层索引性能调优的结合。 4. 学习亚马逊云上的各种云服务。 5. 参与产品需求讨论,提出技术实现方案。 6. 与国内外杰出的开发团队紧密合作,学习代码开发和审查的流程。 We are open to hiring candidates to work out of one of the following locations: Shanghai, CHN
CN, Shanghai
亚马逊云科技上海人工智能实验室OpenSearch 研发团队正在招募应用科学家实习,方向是服务器端开发。OpenSearch是一个开源的搜索和数据分析套件, 它旨在为数据密集型应用构建解决方案,内置高性能、开发者友好的工具,并集成了强大的机器学习、数据处理功能,可以为客户提供灵活的数据探索、丰富和可视化功能,帮助客户从复杂的数据中发现有价值的信息。OpenSearch是现有AWS托管服务(AWS OpenSearch)的基础,OpenSearch核心团队负责维护OpenSearch代码库,他们的目标是使OpenSearch安全、高效、可扩展、可扩展并永远开源。 点击下方链接查看申请手册获得更多信息: https://amazonexteu.qualtrics.com/CP/File.php?F=F_55YI0e7rNdeoB6e Key job responsibilities 在这个实习期间,你将有机会: 1. 使用Java/Kotlin等服务器端技术编写高质量,高性能,安全,可维护和可测试的代码。 2. 了解主流搜索引擎Lucene的原理和应用。 3. 学习亚马逊云上的各种云服务。 4. 参与产品需求讨论,提出技术实现方案。 5. 与国内外杰出的开发团队紧密合作,学习代码开发和审查的流程。 6. 应用先进的人工智能和机器学习技术提升用户体验。 We are open to hiring candidates to work out of one of the following locations: Shanghai, CHN
CN, Shanghai
亚马逊云科技上海人工智能实验室OpenSearch 研发团队正在招募应用科学家实习,方向是服务器端开发。OpenSearch是一个开源的搜索和数据分析套件, 它旨在为数据密集型应用构建解决方案,内置高性能、开发者友好的工具,并集成了强大的机器学习、数据处理功能,可以为客户提供灵活的数据探索、丰富和可视化功能,帮助客户从复杂的数据中发现有价值的信息。OpenSearch是现有AWS托管服务(AWS OpenSearch)的基础,OpenSearch核心团队负责维护OpenSearch代码库,他们的目标是使OpenSearch安全、高效、可扩展、可扩展并永远开源。 点击下方链接查看申请手册获得更多信息: https://amazonexteu.qualtrics.com/CP/File.php?F=F_55YI0e7rNdeoB6e Key job responsibilities 在这个实习期间,你将有机会: • 使用HTML、CSS和TypeScript/Javascript等前端技术开发用户界面。 • 学习使用Node.js 为用户界面提供服务接口。 • 了解并实践工业级前端产品的开发/部署/安全审查/发布流程。 • 了解并实践前端框架React的使用。 • 参与产品需求讨论,提出技术实现方案。 • 与国内外杰出的开发团队紧密合作,学习代码开发和审查的流程。 • 编写高质量,高性能,安全,可维护和可测试的代码。 • 应用先进的人工智能和机器学习技术提升用户体验。 We are open to hiring candidates to work out of one of the following locations: Shanghai, CHN
US, WA, Bellevue
Are you excited about developing generative AI, reinforcement learning and foundation models? Are you looking for opportunities to build and deploy them on real problems at truly vast scale? At Amazon Fulfillment Technologies and Robotics, we are on a mission to build high-performance autonomous decision systems that perceive and act to further improve our world-class customer experience - at Amazon scale. We are looking for an Applied Scientist who will help us build next level simulation and optimization systems with the help of generative AI and LLMs. Together, we will be pushing beyond the state of the art in simulation and optimization of one of the most complex systems in the world: Amazon's Fulfillment Network. Key job responsibilities In this role, you will dive deep into our fulfillment network, understand complex processes and channel your insights to build large scale machine learning models (LLMs, graph neural nets and reinforcement learning) that will be able to understand and optimize the state and future of our buildings, network and orders. You will face a high level of research ambiguity and problems that require creative, ambitious, and inventive solutions. You will work with and in a team of applied scientists to solve cutting edge problems going beyond the published state of the art that will drive transformative change on a truly global scale. A day in the life In this role, you will dive deep into our fulfillment network, understand complex processes and channel your insights to build large scale machine learning models (LLMs, graph neural nets and reinforcement learning) that will be able to understand and optimize the state and future of our buildings, network and orders. You will face a high level of research ambiguity and problems that require creative, ambitious, and inventive solutions. You will work with and in a team of applied scientists to solve cutting edge problems going beyond the published state of the art that will drive transformative change on a truly global scale. A day in the life Amazon offers a full range of benefits that support you and eligible family members, including domestic partners and their children. Benefits can vary by location, the number of regularly scheduled hours you work, length of employment, and job status such as seasonal or temporary employment. The benefits that generally apply to regular, full-time employees include: 1. Medical, Dental, and Vision Coverage 2. Maternity and Parental Leave Options 3. Paid Time Off (PTO) 4. 401(k) Plan If you are not sure that every qualification on the list above describes you exactly, we'd still love to hear from you! At Amazon, we value people with unique backgrounds, experiences, and skillsets. If you’re passionate about this role and want to make an impact on a global scale, please apply! About the team Amazon Fulfillment Technologies (AFT) powers Amazon’s global fulfillment network. We invent and deliver software, hardware, and data science solutions that orchestrate processes, robots, machines, and people. We harmonize the physical and virtual world so Amazon customers can get what they want, when they want it. The AFT AI team has deep expertise developing cutting edge AI solutions at scale and successfully applying them to business problems in the Amazon Fulfillment Network. These solutions typically utilize machine learning and computer vision techniques, applied to text, sequences of events, images or video from existing or new hardware. We influence each stage of innovation from inception to deployment, developing a research plan, creating and testing prototype solutions, and shepherding the production versions to launch. We are open to hiring candidates to work out of one of the following locations: Bellevue, WA, USA
LU, Luxembourg
Pooling Req - JKU Linz Pooling Req - JKU Linz Pooling Req - JKU Linz Pooling Req - JKU Linz Pooling Req - JKU Linz Pooling Req - JKU Linz Pooling Req - JKU Linz Pooling Req - JKU Linz Pooling Req - JKU Linz Pooling Req - JKU Linz We are open to hiring candidates to work out of one of the following locations: Luxembourg, LUX
AU, VIC, Melbourne
AWS Sales, Marketing, and Global Services (SMGS) is responsible for driving revenue, adoption, and growth from the largest and fastest growing small- and mid-market accounts to enterprise-level customers including public sector. The AWS Global Support team interacts with leading companies and believes that world-class support is critical to customer success. AWS Support also partners with a global list of customers that are building mission-critical applications on top of AWS services. The Generative Artificial Intelligence (AI) Innovation Center team at AWS provides opportunities to innovate in a fast-paced organization that contributes to game-changing projects and technologies leveraging cutting-edge generative AI algorithms. As an Applied Scientist, you'll partner with technology and business teams to build solutions that surprise and delight our customers. We’re looking for Applied Scientists capable of using generative AI and other ML techniques to design, evangelize, and implement state-of-the-art solutions for never-before-solved problems. Key job responsibilities - Collaborate with scientists and engineers to research, design and develop cutting-edge generative AI algorithms to address real-world challenges - Work across customer engagement to understand what adoption patterns for generative AI are working and rapidly share them across teams and leadership - 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 for generative AI - 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 Here at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon’s culture of inclusion is reinforced within our 16 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust. About the team 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. What if I don’t meet all the requirements? That’s okay! We hire people who have a passion for learning and are curious. You will be supported in your career development here at AWS. You will have plenty of opportunities to build your technical, leadership, business and consulting skills. Your onboarding will set you up for success, including a combination of formal and informal training. You’ll also have a chance to gain AWS certifications and access mentorship programs. You will learn from and collaborate with some of the brightest technical minds in the industry today. We are open to hiring candidates to work out of one of the following locations: Melbourne, VIC, AUS
AU, NSW, Sydney
Amazon launched the Generative AI Innovation Center (GAIIC) in Jun 2023 to help AWS customers accelerate the use of Generative AI to solve business and operational problems and promote innovation in their organization (https://press.aboutamazon.com/2023/6/aws-announces-generative-ai-innovation-center). GAIIC provides opportunities to innovate in a fast-paced organization that contributes to game-changing projects and technologies that get deployed on devices and in the cloud. As an Applied Science Manager in GAIIC, you'll partner with technology and business teams to build new GenAI solutions that delight our customers. You will be responsible for directing a team of data/research/applied scientists, deep learning architects, and ML engineers to build generative AI models and pipelines, and deliver state-of-the-art solutions to customer’s business and mission problems. Your team will be working with terabytes of text, images, and other types of data to address real-world problems. The successful candidate will possess both technical and customer-facing skills that will allow you to be the technical “face” of AWS within our solution providers’ ecosystem/environment as well as directly to end customers. You will be able to drive discussions with senior technical and management personnel within customers and partners, as well as the technical background that enables them to interact with and give guidance to data/research/applied scientists and software developers. The ideal candidate will also have a demonstrated ability to think strategically about business, product, and technical issues. Finally, and of critical importance, the candidate will be an excellent technical team manager, someone who knows how to hire, develop, and retain high quality technical talent. AWS Sales, Marketing, and Global Services (SMGS) is responsible for driving revenue, adoption, and growth from the largest and fastest growing small- and mid-market accounts to enterprise-level customers including public sector. The AWS Global Support team interacts with leading companies and believes that world-class support is critical to customer success. AWS Support also partners with a global list of customers that are building mission-critical applications on top of AWS services. About the team 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. We are open to hiring candidates to work out of one of the following locations: Sydney, NSW, AUS
US, WA, Seattle
Amazon is one of the most popular sites in the US. Our product search engine, one of the most heavily used services in the world, indexes billions of products and serves hundreds of millions of customers world-wide. Our team leads the science and analytics efforts for the search page and we own multiple aspects of understanding how we can measure customer satisfaction with our experiences. This include building science based insights and novel metrics to define and track customer focused aspects. We are working on a new measurement framework to better quantify and qualify the quality of the search customer experience and are looking for a Senior Applied Scientist to lead the development and implementation of different signals for this framework and tackle new and uncharted territories for search engines using LLMs. Key job responsibilities We are looking for an experienced Sr. Applied Scientist to lead LLM based signals development and data analytics and drive critical product decisions for Amazon Search. In a fast-paced and ambiguous environment, you will perform multiple large, complex, and business critical analyses that will inform product design and business priorities. You will design and build AI based science solutions to allow routine inspection and deep business understanding as the search customer experience is being transformed. Keeping a department-wide view, you will focus on the highest priorities and constantly look for scale and automation, while making technical trade-offs between short term and long-term needs. With your drive to deliver results, you will quickly analyze data and understand the current business challenges to assess the feasibility of different science projects as well as help shape the analytics roadmap of the Science and Analytics team for Search CX. Your desire to learn and be curious will help us look around corners for improvement opportunities and more efficient metrics development. In this role, you will partner with data engineers, business intelligence engineers, product managers, software engineers, economists, and other scientists. A day in the life You are have expertise in Machine learning and statistical models. You are comfortable with a higher degree of ambiguity, knows when and how to be scrappy, build quick prototypes and proofs of concepts, innate ability to see around corners and know what is coming, define a long-term science vision, and relish the idea of solving problems that haven’t been solved at scale. As part of our journey to learn about our data, some opportunities may be a dead end and you will balancing unknowns with delivering results for our customers. Along the way, you’ll learn a ton, have fun and make a positive impact at scale. About the team Joining this team, you’ll experience the benefits of working in a dynamic, entrepreneurial environment, while leveraging the resources of Amazon.com (AMZN), Earth's most customer-centric company and one of the world's leading internet companies. We provide a highly customer-centric, and team-oriented environment. We are open to hiring candidates to work out of one of the following locations: Seattle, WA, USA