2020 Amazon Research Awards recipients announced

ARA funds nearly twice as many awards as in previous year; 100 award recipients represent 59 universities in 13 countries.

In March 2021, Amazon notified applicants that they were recipients of the 2020 Amazon Research Awards, a program that provides unrestricted funds and AWS Promotional Credits to academic researchers investigating research topics across a number of disciplines.

Today, we’re publicly announcing the 100 award recipients who represent 59 universities in 13 countries. This round, ARA received a record number of submissions and funded nearly twice as many awards as the previous year. Each award is intended to support the work of one to two graduate students or postdoctoral students for one year, under the supervision of a faculty member.

ARA is funding awards under five call for proposals: AI for Information Security, Alexa Fairness in AI, AWS AI, AWS Automated Reasoning, and Robotics. Proposals were reviewed for the quality of their scientific content, their creativity, and their potential to impact both the research community, and society more generally. Theoretical advances, creative new ideas, and practical applications were all considered.

Recipients have access to more than 200 Amazon public datasets, and can utilize AWS AI/ML services and tools through their AWS Promotional Credits. Recipients also are assigned an Amazon research contact who offers consultation and advice along with opportunities to participate in Amazon events and training sessions.

Additionally, Amazon encourages the publication of research results, presentations of research at Amazon offices worldwide, and the release of related code under open-source licenses.

“The 2020 Amazon Research Awards recipients represent a distinguished array of academic researchers who are pursuing research across areas such as ML algorithms and theory, fairness in AI, computer vision, natural language processing, edge computing, and medical research,” said Bratin Saha, vice president of AWS Machine Learning Services. “We are excited by the depth and breadth of their proposals, as well as the opportunity to advance the science through strengthened connections among academic researchers, their institutions, and our research teams.”

“As we enter into this golden age of robotics, we do so with our university partners. Not only are they shaping what is possible in robotics, they are inspiring many next- generation roboticists with their incredible creations and front-line teachings,” said Tye Brady, chief technologist for Amazon Robotics. “Our grant recipients are not only pursuing cutting-edge research that will benefit society, but perhaps more importantly are helping students from across the globe pursue a career in science and engineering.”

ARA funds proposals up to four times a year in a variety of research areas. Applicants are encouraged to visit the ARA call for proposals page for more information or send an email to be notified of future open calls.

Below is the list of 2020 award recipients, presented in alphabetical order.

RecipientUniversityResearch title
Vikram AdveUniversity of Illinois Urbana-ChampaignExtending the LLVM compiler infrastructure for tensor architectures
Pulkit AgrawalMassachusetts Institute of TechnologyA framework for multi-step planning for manipulating rigid objects
Ron AlterovitzUniversity of North Carolina at Chapel HillCloud-based motion planning: an enabling technology for next-generation autonomous robots
Jimmy BaUniversity of TorontoModel-based reinforcement learning with causal world models
Saurabh BagchiPurdue University—West LafayetteContent and contention-aware approximate streaming video analytics for edge devices
David Baker EffendiStellenbosch UniversityDataflow analysis using code property graphs, graph databases and synchronized pushdown systems
Sivaraman BalakrishnanCarnegie Mellon UniversityFoundations of robust machine learning: from principled approaches to practice
Elias BareinboimColumbia UniversityOff-policy evaluation through causal modeling
Clark BarrettStanford UniversityModel-based testing of SMT solvers
Lars BirkedalAarhus UniversityModular reasoning about distributed systems: higher-order distributed separation logic
David BleiColumbia UniversityNew directions in observational causal inference
Eric BoddenPaderborn UniversityHybridCG — dynamically-enriched call-Graph generation of Java enterprise applications
Legand BurgeHoward UniversityVoice-FAQ: artificial intelligence for triaging cognitive decline through modeling vocal prosody and facial expressions
James CaverleeTexas A&M University, College StationFairness in recommendation without demographics
Changyou ChenUniversity at BuffaloScaling up human-action analysis systems
Danqi ChenPrinceton UniversityBuilding broad-coverage, structured dense knowledge bases for natural language processing tasks
Helen ChenUniversity of WaterlooOptimizing pretrained clinical embeddings for automatic COVID-related ICD coding
Yiran ChenDuke UniversityPrivacy-preserving representation learning on graphs — a mutual information perspective
Margarita ChliETH ZurichVision-based emergency landing in urban environments using reinforcement learning and deep learning
Kyunghyun ChoNew York UniversityIndependently controllable attributes for controllable neural text generation
Carlo CilibertoUniversity College LondonOptimal transport for meta-learning
Loris D'AntoniUniversity of Wisconsin–MadisonCorrect-by-construction IAM policies
David DanksCarnegie Mellon UniversityAn integrated framework for understanding human-AI hybrid decision-making
Suhas DiggaviUniversity of California, Los AngelesCompressed private and secure distributed edge learning
Greg DurrettUniversity of Texas At AustinMaking conditional text generation fair and factual
Sergio EscaleraUniversitat de Barcelona and Computer Vision CenterPortable virtual try-on for smart devices
Jan FaiglCzech Technical University in PragueCommunication maps building in subterranean environments
Pietro FerraraCa' Foscari University of VeniceIAM access control policies verification and inference
Katerina FragkiadakiCarnegie Mellon UniversityGeneralizing manipulation across objects, configurations and views using a visually-grounded library of behaviors
Guillermo GallegoTechnical University of BerlinOnline in-hand object tracking and grasp failure detection with an event-based camera
Grace GaoStanford UniversityTrustworthy autonomous vehicle localization using a joint model-driven and data-driven approach
Stephanie GilHarvard UniversityEnabling the next generation of coordinated robots: scalable real-time decision making
Luca GiuggioliUniversity of BristolMulti-robot online exploration in extreme unbounded environments through adaptive socio-spatial ordering
Jorge GoncalvesUniversity of MelbourneIntegrated qualification test framework to measure crowd worker quality and assign or recommend heterogeneous tasks
Ananth GramaPurdue University—West LafayetteScaling causal inference to explainable clinical recommendations
Grace GuUniversity of California, BerkeleySurrogate machine learning model and quasi-static simulation of pneumatically actuated robotic devices
Ronghui GuColumbia UniversityMicroverification of the Linux KVM hypervisor: proving VM confidentiality and integrity
Aarti GuptaPrinceton UniversityLearning abstract specifications from distributed program implementations
Saurabh GuptaUniversity of Illinois Urbana-ChampaignSelf-supervised discovery of object states and transitions from unlabeled videos
Daniel HaraborMonash UniversityAnytime constraint-based multi-agent pathfinding
Hynek HermanskyJohns Hopkins UniversityMultistream lifelong federated learning for machine recognition of speech
Bin HuUniversity of Illinois Urbana-ChampaignProvably robust adversarial reinforcement learning for sequential decision making in safety-critical environments
Lifu HuangVirginia TechEvent-centric temporal and causal knowledge acquisition and generalization for natural language understanding
Dinesh JayaramanUniversity of PennsylvaniaLearning modular dynamics models for plug-and-play visual control
Sven KoenigUniversity of Southern CaliforniaImproving planning and plan execution for warehouse automation
Laura KovacsTU WienFOREST: first-order reasoning for ensuring system security
Arun KumarUniversity of California, San DiegoImproving automated feature type inference for AutoML on tabular data
Himabindu LakkarajuHarvard UniversityTowards reliable and robust model explanations
Kevin Leyton-BrownUniversity of British ColumbiaAutomated machine learning for tabular datasets using hyperband embedded reinforcement learning
Bo LiUniversity of Illinois Urbana-ChampaignMachine learning evaluation as a service for robustness, fairness, and privacy utilities
Ke LiUniversity of ExeterMany hands make work light: multi-task deep semantic learning for testing web application firewalls
Zhiqiang LinOhio State UniversityType-aware recovery of symbol names in binary code: a machine learning based approach
Jeffrey LiuMassachusetts Institute of TechnologyIntegrating the low altitude disaster imagery (LADI) dataset into the MIT Beaver Works curriculum
Michael MahoneyUniversity of California, BerkeleySystematic methods for efficient inference and training of neural networks
Radu MarculescuUniversity of TexasNew directions for 3D object detection: distributed inference on edge devices using knowledge distillation
Ruben MartinsCarnegie Mellon UniversityImproving performance and trust of MaxSAT solvers
Jiri MatasCzech Technical University in PragueTraining neural networks on non-differentiable losses
Michael MilfordQueensland University of TechnologyComplementarity-aware multi-process fusion for long term localization
Heather MillerCarnegie Mellon UniversityDirected automated explicit-state model checking for distributed applications
Ndapa NakasholeUniversity of California, San DiegoLearning representations for voice-based conversational agents for older adults
Shrikanth NarayananUniversity of Southern CaliforniaToward inclusive human-AI conversational experiences for children
Lerrel PintoNew York UniversityLearning to manipulate deformable objects through robust simulations
Ravi RamamoorthiUniversity of California, San DiegoSparse multi-view object acquisition using learned volumetric representations
Philip ResnikUniversity of Maryland, College ParkAdvanced topic modeling to support the understanding of COVID-19 and its effects
Daniela RusMassachusetts Institute of TechnologyLearning to plan through imagined self-play for multi-agent system
Supreeth ShashikumarUniversity of California, San DiegoPrivacy preserving continual learning with applications to critical care
Robert ShepherdCornell UniversityEnduring and adaptive robots via electrochemical blood
Cong ShiUniversity of Michigan, Ann ArborMachine learning for personalized assortment optimization
Florian ShkurtiUniversity of TorontoGenerating physically realizable adversarial driving scenarios via differentiable physics and rendering simulators
Abhinav ShrivastavaUniversity of Maryland, College ParkThe pursuit of knowledge: discovering and localizing new concepts using dual memory
Roland SiegwartETH ZurichSafe self-calibration of hybrid aerial vehicles
Sameer SinghUniversity of California, IrvineDetecting and fixing vulnerabilities in NLP models via semantic perturbations and tracing data influence
Noah SmithUniversity of Washington - SeattleLanguage model customization
Mahdi SoltanolkotabiUniversity of Southern CaliforniaArtificial intelligence for fast and portable medical imaging (with limited training data)
Seung Woo SonUniversity of Massachusetts LowellReliable and accurate anomaly detection in edge nodes using sparsity profile
Dawn SongUniversity of California, BerkeleyKnowledge-enhanced cyber threat hunting
Dezhen SongTexas A&M University, College StationOptoacoustic material and structure pretouch sensing at robot fingertip
Shuran SongColumbia UniversityDexterity through diversity:learning a generalizable grasping policy for diverse end-effectors
Yizhou SunUniversity of California, Los AngelesAccelerating graph neural network training
Russ TedrakeMassachusetts Institute of TechnologyIntuitive physics for manipulation
James TompkinBrown UniversityReal-time multi-camera fusion for unoccluded VR robot teleoperation
Emina TorlakUniversity of Washington - SeattleAutomated verification of JIT compilers for BPF
Marynel VazquezYale UniversityEvaluating social robot navigation via online human-driven simulations
Nisheeth VishnoiYale UniversityFair and error-resilient algorithms for AI and ML
Gang WangUniversity of Illinois at Urbana–ChampaignCombating concept drift in security applications via proactive data synthesis
Hao WangRutgers University-New BrunswickStructured domain adaptation with applications to personalization and forecasting
James WangPennsylvania State UniversityAffective and social interaction between human and intelligent machine
Gloria WashingtonHoward UniversityTowards identification of uncomfortable speech in conversations
Chuan WuThe University of Hong KongCompilation optimization in distributed DNN training: joining OP and tensor fusion/partition
Eugene WuColumbia UniversityHuman-in-the-loop data debugging for ML-oriented analytics
Jiajun WuStanford UniversityImplicit dynamic scene representation learning for robotics
Ming-Ru WuDana-Farber Cancer InstituteFrom bench to clinic – machine-learning based cancer immunotherapy design
Diyi YangGeorgia Institute of TechnologyAbstractive conversation summarization at scale
Sixian YouMassachusetts Institute of TechnologyAI-driven label-free histology for cancer diagnosis
Jingjin YuRutgers University-New BrunswickPushing the limits of efficient and optimal multi-agent path finding through exploring space utilization optimization and adaptive planning horizon heuristics
Rui ZhangPennsylvania State UniversityBuilding robust conversational question answering systems over databases of tabular data
Yu ZhangUniversity of South FloridaDesign of an automated advanced air mobility flight planning system (AAFPS)
Yuke ZhuUniversity of Texas at AustinLearning implicit shape affordance for grasping and manipulation
Marinka ZitnikHarvard UniversityActionable graph learning for finding cures for emerging diseases
James ZouStanford UniversityHow to make AI forget you? Efficiently removing individuals’ data from machine learning models

Related content

US, CA, Santa Clara
Job summaryAmazon is looking for a passionate, talented, and inventive Applied Scientist with a strong machine learning background to help build industry-leading language technology.Our mission is to provide a delightful experience to Amazon’s customers by pushing the envelope in Natural Language Processing (NLP), Natural Language Understanding (NLU), Dialog management, conversational AI and 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, as well as contributing to the wider research community. You will gain hands on experience with Amazon’s heterogeneous text and structured data sources, and large-scale computing resources to accelerate advances in language understanding.We are hiring primarily in Conversational AI / Dialog System Development areas: NLP, NLU, Dialog Management, NLG.This role can be based in NYC, Seattle or Palo Alto.Inclusive Team CultureHere 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.Work/Life BalanceOur team puts a high value on work-life balance. It isn’t about how many 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 believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives.Mentorship & Career GrowthOur team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded engineer and enable them to take on more complex tasks in the future.
US, NY, New York
Job summaryAmazon is looking for a passionate, talented, and inventive Applied Scientist with a strong machine learning background to help build industry-leading language technology.Our mission is to provide a delightful experience to Amazon’s customers by pushing the envelope in Natural Language Processing (NLP), Natural Language Understanding (NLU), Dialog management, conversational AI and 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, as well as contributing to the wider research community. You will gain hands on experience with Amazon’s heterogeneous text and structured data sources, and large-scale computing resources to accelerate advances in language understanding.We are hiring primarily in Conversational AI / Dialog System Development areas: NLP, NLU, Dialog Management, NLG.This role can be based in NYC, Seattle or Palo Alto.Inclusive Team CultureHere 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.Work/Life BalanceOur team puts a high value on work-life balance. It isn’t about how many 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 believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives.Mentorship & Career GrowthOur team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded engineer and enable them to take on more complex tasks in the future.
US, WA, Seattle
Job summaryWorkforce Staffing (WFS) brings together the workforce powering Amazon’s ability to delight customers: the Amazon Associate. With over 1M hires, WFS supports sourcing, hiring, and developing the best talent to work in our fulfillment centers, sortation centers, delivery stations, shopping sites, Prime Air locations, and more.WFS' Funnel Science and Analytics team is looking for a Research Scientist. This individual will be responsible for conducting experiments and evaluating the impact of interventions when conducting experiments is not feasible. The perfect candidate will have the applied experience and the theoretical knowledge of policy evaluation and conducting field studies.Key job responsibilitiesAs a Research Scientist (RS), you will do causal inference, design studies and experiments, leverage data science workflows, build predictive models, conduct simulations, create visualizations, and influence science and analytics practice across the organization.Provide insights by analyzing historical data from databases (Redshift, SQL Server, Oracle DW, and Salesforce).Identify useful research avenues for increasing candidate conversion, test, and create well written documents to communicate to technical and non-technical audiences.About the teamFunnel Science and Analytics team finds ways to maximize the conversion and early retention of every candidate who wants to be an Amazon Associate. By focusing on our candidates, we improve candidate and business outcomes, and Amazon takes a step closer to being Earth’s Best Employer.
US, CA, Santa Clara
Job summaryAWS AI/ML is looking for world class scientists and engineers to join its AI Research and Education group working on building automated ML solutions for planetary-scale sustainability and geospatial applications. Our team's mission is to develop ready-to-use and automated solutions that solve important sustainability and geospatial problems. We live in a time wherein geospatial data, such as climate, agricultural crop yield, weather, landcover, etc., has become ubiquitous. Cloud computing has made it easy to gather and process the data that describes the earth system and are generated by satellites, mobile devices, and IoT devices. Our vision is to bring the best ML/AI algorithms to solve practical environmental and sustainability-related R&D problems at scale. Building these solutions require a solid foundation in machine learning infrastructure and deep learning technologies. The team specializes in developing popular open source software libraries like AutoGluon, GluonCV, GluonNLP, DGL, Apache/MXNet (incubating). Our strategy is to bring the best of ML based automation to the geospatial and sustainability area.We are seeking an experienced Applied Scientist for the team. This is a role that combines science knowledge (around machine learning, computer vision, earth science), technical strength, and product focus. It will be your job to develop ML system and solutions and work closely with the engineering team to ship them to our customers. You will interact closely with our customers and with the academic and research communities. You will be at the heart of a growing and exciting focus area for AWS and work with other acclaimed engineers and world famous scientists. You are also expected to work closely with other applied scientists and demonstrate Amazon Leadership Principles (https://www.amazon.jobs/en/principles). Strong technical skills and experience with machine learning and computer vision are required. Experience working with earth science, mapping, and geospatial data is a plus. Our customers are extremely technical and the solutions we build for them are strongly coupled to technical feasibility.About the teamInclusive Team CultureAt 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 14 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust. Work/Life BalanceOur team puts a high value on work-life balance. It isn’t about how many 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 believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives.Mentorship & Career GrowthOur team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. Our senior members enjoy one-on-one mentoring. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded scientist and enable them to take on more complex tasks in the future.Interested in this role? Reach out to the recruiting team with questions or apply directly via amazon.jobs.
US, CA, Santa Clara
Job summaryAWS AI/ML is looking for world class scientists and engineers to join its AI Research and Education group working on building automated ML solutions for planetary-scale sustainability and geospatial applications. Our team's mission is to develop ready-to-use and automated solutions that solve important sustainability and geospatial problems. We live in a time wherein geospatial data, such as climate, agricultural crop yield, weather, landcover, etc., has become ubiquitous. Cloud computing has made it easy to gather and process the data that describes the earth system and are generated by satellites, mobile devices, and IoT devices. Our vision is to bring the best ML/AI algorithms to solve practical environmental and sustainability-related R&D problems at scale. Building these solutions require a solid foundation in machine learning infrastructure and deep learning technologies. The team specializes in developing popular open source software libraries like AutoGluon, GluonCV, GluonNLP, DGL, Apache/MXNet (incubating). Our strategy is to bring the best of ML based automation to the geospatial and sustainability area.We are seeking an experienced Applied Scientist for the team. This is a role that combines science knowledge (around machine learning, computer vision, earth science), technical strength, and product focus. It will be your job to develop ML system and solutions and work closely with the engineering team to ship them to our customers. You will interact closely with our customers and with the academic and research communities. You will be at the heart of a growing and exciting focus area for AWS and work with other acclaimed engineers and world famous scientists. You are also expected to work closely with other applied scientists and demonstrate Amazon Leadership Principles (https://www.amazon.jobs/en/principles). Strong technical skills and experience with machine learning and computer vision are required. Experience working with earth science, mapping, and geospatial data is a plus. Our customers are extremely technical and the solutions we build for them are strongly coupled to technical feasibility.About the teamInclusive Team CultureAt 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 14 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust. Work/Life BalanceOur team puts a high value on work-life balance. It isn’t about how many 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 believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives.Mentorship & Career GrowthOur team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. Our senior members enjoy one-on-one mentoring. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded scientist and enable them to take on more complex tasks in the future.Interested in this role? Reach out to the recruiting team with questions or apply directly via amazon.jobs.
US, WA, Seattle
Job summaryHow can we create a rich, data-driven shopping experience on Amazon? How do we build data models that helps us innovate different ways to enhance customer experience? How do we combine the world's greatest online shopping dataset with Amazon's computing power to create models that deeply understand our customers? Recommendations at Amazon is a way to help customers discover products. Our team's stated mission is to "grow each customer’s relationship with Amazon by leveraging our deep understanding of them to provide relevant and timely product, program, and content recommendations". We strive to better understand how customers shop on Amazon (and elsewhere) and build recommendations models to streamline customers' shopping experience by showing the right products at the right time. Understanding the complexities of customers' shopping needs and helping them explore the depth and breadth of Amazon's catalog is a challenge we take on every day. Using Amazon’s large-scale computing resources you will ask research questions about customer behavior, build models to generate recommendations, and run these models directly on the retail website. You will participate in the Amazon ML community and mentor Applied Scientists and software development engineers with a strong interest in and knowledge of ML. Your work will directly benefit customers and the retail business and you will measure the impact using scientific tools. We are looking for passionate, hard-working, and talented Applied scientist who have experience building mission critical, high volume applications that customers love. You will have an enormous opportunity to make a large impact on the design, architecture, and implementation of cutting edge products used every day, by people you know.Key job responsibilitiesScaling state of the art techniques to Amazon-scaleWorking independently and collaborating with SDEs to deploy models to productionDeveloping long-term roadmaps for the team's scientific agendaDesigning experiments to measure business impact of the team's effortsMentoring scientists in the departmentContributing back to the machine learning science community
US, NY, New York City
Job summaryAmazon Web Services is looking for world class scientists to join the Security Analytics and AI Research team within AWS Security Services. This group is entrusted with researching and developing core data mining and machine learning algorithms for various AWS security services like GuardDuty (https://aws.amazon.com/guardduty/) and Macie (https://aws.amazon.com/macie/). In this group, you will invent and implement innovative solutions for never-before-solved problems. If you have passion for security and experience with large scale machine learning problems, this will be an exciting opportunity.The AWS Security Services team builds technologies that help customers strengthen their security posture and better meet security requirements in the AWS Cloud. The team interacts with security researchers to codify our own learnings and best practices and make them available for customers. We are building massively scalable and globally distributed security systems to power next generation services.Inclusive Team Culture 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 14 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust. Work/Life Balance Our team puts a high value on work-life balance. It isn’t about how many 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 believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives. Mentorship & Career Growth Our team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. We care about your career growth and strive to assign projects based on what will help each team member develop and enable them to take on more complex tasks in the future.A day in the lifeAbout the hiring groupJob responsibilities* Rapidly design, prototype and test many possible hypotheses in a high-ambiguity environment, making use of both quantitative and business judgment.* Collaborate with software engineering teams to integrate successful experiments into large scale, highly complex production services.* Report results in a scientifically rigorous way.* Interact with security engineers, product managers and related domain experts to dive deep into the types of challenges that we need innovative solutions for.
US, WA, Bellevue
Job summary Do you want to join Alexa AI -- the science team behind Amazon’s intelligence voice assistance system? Do you want to utilize cutting-edge deep-learning and machine learning algorithms to delight millions of Alexa users around the world? If your answers to these questions are “yes”, then come join us at the Alexa Artificial Intelligence (AI) team, which is in charge of improving Alexa user satisfaction through real-time metrics monitoring and continuous closed-loop learning. The team owns the modules that reduce user perceived defects and frictions through utterance reformulation, contextual and personalized hypothesis ranking. Key job responsibilitiesThe Alexa AI team is interested in an Applied Scientist to work alongside a team of experienced machine/deep learning scientists and engineers. The ideal Candidate will create data driven machine learning models and solutions on tasks such as sequence-to-sequence query reformulation, graph feature embedding, personalized ranking, etc..Additional responsibilities include: Analyze, understand, and model user-behavior and the user-experience based on large scale data, to detect key factors causing satisfaction and dissatisfaction (SAT/DSAT)Build and measure novel online & offline metrics for personal digital assistants and user scenarios, on diverse devices and endpointsCreate and innovate deep learning and/or machine learning based algorithms for utterance reformulation and contextual hypothesis ranking to reduce user dissatisfaction in various scenariosPerform model/data analysis and monitor user-experienced based metrics through online A/B testingResearch and implement novel machine learning and deep learning algorithms and models
US, CA, Santa Clara
Job summaryAmazon is looking for a customer focused, analytically and technically skilled Data Sciences Leader for Amazon Physical Stores Business. We’re trying to optimize shopping experience for Amazon’s Customers in the Physical retail space. This role will be a key member of the core Analytics team, based in Seattle, WA. The successful candidate will be a self-starter comfortable with ambiguity, with strong attention to detail, an ability to work in a fast-paced, high-energy and ever-changing environment. The drive and capability to shape the business group strategy is a must.In this role, you will work to establish world class data science, analytics and reporting for Amazonians as part of building the Physical Retail experience for our customers. This key role will work closely with internal partners to assist in developing and managing analytic solutions. Your team will work closely with Product Managers, Software Engineers, and Program Managers to develop statistical models, design and run experiments, and find new ways to optimize customer shopping and product experience. You and your team 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. We see a high potential for influence and growth in this role as we transform our data into actionable insights to continue to fuel the growth of this business. Key job responsibilities• Manage a team of data scientists, identify opportunities and develop data science strategies.• Translate business questions and concerns into specific analytical questions that can be answered with available data using statistical methods.• Apply Statistical and Machine Learning methods to specific business problems and data.• Ensure data quality throughout all stages of acquisition and processing, including such areas as data sourcing/collection, ground truth generation, normalization, transformation, cross-lingual alignment/mapping, etc.• Communicate proposals and results in a clear manner backed by data and coupled with actionable conclusions to drive business decisions.• Collaborate with colleagues from multidisciplinary science, engineering and business backgrounds.• Work with engineers to develop efficient data querying and modeling infrastructure.• Manage your own process. Prioritize and execute on high impact projects, triage external requests, and ensure to deliver projects in time.• Utilizing code (Python, R, Scala, etc.) for analyzing data and building statistical models.
US, NC, Virtual Location - N Carolina
Job summaryWant to help the largest global enterprises derive business value through the adoption of Artificial Intelligence (AI) and Machine Learning (ML)? Excited by using massive amounts of disparate data to develop ML models? Eager to learn to apply ML to a diverse array of enterprise use cases? Thrilled to be a part of Amazon who has been pioneering and shaping the world’s AI/ML technology for decades? At Amazon Web Services (AWS), we are helping large enterprises build ML models on the AWS Cloud. We are applying predictive technology to large volumes of data and against a wide spectrum of problems. AWS Professional Services works together with AWS customers to address their business needs using AI solutions. AWS Professional Services is a unique consulting team. We pride ourselves on being customer obsessed and highly focused on the AI enablement of our customers. If you have experience with AI, including building ML models, we’d like to have you join our team. You will get to work with an innovative company, with great teammates, and have a lot of fun helping our customers. A successful candidate will be a person who enjoys diving deep into data, doing analysis, discovering root causes, and designing long-term solutions. Major responsibilities include:Assist customers by being able to deliver a ML project from beginning to end, including understanding the business need, aggregating data, exploring data, building & validating predictive models, and deploying completed models with concept-drift monitoring and retraining to deliver business impact to the organizationUse AWS AI services (e.g., Personalize), ML platforms (SageMaker), and frameworks (e.g., MXNet, TensorFlow, PyTorch, SparkML, scikit-learn) to help our customers build ML modelsResearch and implement novel ML approaches, including hardware optimizations on platforms such as AWS InferentiaWork with our other Professional Services consultants (Big Data, IoT, HPC) to analyze, extract, normalize, and label relevant data, and with our Professional Services engineers to operationalize customers’ models after they are prototypedInclusive Team Culture Here at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have thirteen employee-led affinity groups, reaching 85,000 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. Work/Life Balance Our team puts a high value on work-life harmony. Striking a healthy balance between your personal and professional life is crucial to your happiness and success here. We are a customer-obsessed organization—leaders start with the customer and work backwards. They work vigorously to earn and keep customer trust. As such, this is a customer facing role in a hybrid delivery model. Project engagements include remote delivery methods and onsite engagement that will include travel to customer locations as needed. Mentorship & Career Growth Our team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded professional and enable them to take on more complex tasks in the future. This is a customer-facing role and you will be required to travel to client locations and deliver professional services as needed.This position requires the candidate selected be a US citizen because it provides services under a federal government contract with clearance requirements. This position is limited to individuals who can obtain and maintain the federal government clearance required by the contract.In compliance with the U.S. government requirement that employees of its contractors receive the COVID-19 vaccine if those employees work on or in connection with U.S. government contracts, this position may require that the candidate selected be fully vaccinated against COVID-19. A person is considered fully vaccinated by completing the full regimen of the COVID-19 vaccine (two doses for Pfizer or Moderna and one dose for Johnson & Johnson).