amazon research awards recipients logo.jpg
Amazon today publicly announced 74 recipients from the Amazon Research Awards Fall 2021 call for proposals. The recipients, who represent 51 universities in 17 countries, have access to more than 300 Amazon public datasets, and can utilize AWS AI/ML services and tools.

75 Amazon Research Awards recipients announced

The awardees represent 52 universities in 17 countries. Recipients have access to more than 300 Amazon public datasets, and can utilize AWS AI/ML services and tools.

The Amazon Research Awards is 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 75 award recipients who represent 52 universities in 17 countries. 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.

Top row, left to right: Aws Albarghouthi, Nada Amin, Clark Barrett, Ivan Beschastnikh, William Bowman, Yinzhi Cao, Trevor Carlson, Marsha Chechik; second row, left to right: Cas Cremers, Derek Dreyer, Marcelo Frias, Sicun Gao, Roberto Giacobazzi, Ronghui Gu, Jean-Baptiste Jeannin, Steve Ko; third row, left to right: James Noble, Rohan Padhye, Pavithra Prabhakar, Francesco Ranzato, Talia Ringer, Camilo Rocha, Andrei Sabelfeld, Ilya Sergey; and bottom row, left to right: Michele Sevegnani, Fu Song, Zhendong Su, Daniel Varro, Yakir Vizel, Thomas Wies, Anton Wijs, and Meng Xu.
Top row, left to right: Aws Albarghouthi, Nada Amin, Clark Barrett, Ivan Beschastnikh, William Bowman, Yinzhi Cao, Trevor Carlson, Marsha Chechik; second row, left to right: Cas Cremers, Derek Dreyer, Marcelo Frias, Sicun Gao, Roberto Giacobazzi, Ronghui Gu, Jean-Baptiste Jeannin, Steve Ko; third row, left to right: James Noble, Rohan Padhye, Pavithra Prabhakar, Francesco Ranzato, Talia Ringer, Camilo Rocha, Andrei Sabelfeld, Ilya Sergey; and bottom row, left to right: Michele Sevegnani, Fu Song, Zhendong Su, Daniel Varro, Yakir Vizel, Thomas Wies, Anton Wijs, and Meng Xu are among the recipients from the Amazon Research Awards Fall 2021 call for proposals under the Automated Reasoning CFP.

This announcement includes awards funded under seven call for proposals during the Fall 2021 cycle: AI for Information Security, Amazon Device Security and Privacy, Amazon Payments, AWS Automated Reasoning, Data for Social Sustainability, Prime Video, 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.

Top row, left to right: Nora Ayanian, Nicola Bezzo, Luca Carlone, Venanzio Cichella, Jia Deng, Nima Fazeli, Maani Ghaffari-Jadidi; second row, left to right: Grace Gu, Leonidas Guibas, Felix Heide, Ralph Hollis, Robert Katzschmann, Sven Koenig, George Konidaris; third row, left to right: Sergey Levine, Jennifer Lewis, Maja Matarić, Jan Peters, Lerrel Pinto, Robert Platt, Nancy Pollard; and bottom row, left to right: Alessandro Rizzo, Oren Salzman, Roland Siegwart, Pratap Tokekar, James Wang, Shenlong Wang, and Yuke Zhu.
Top row, left to right: Nora Ayanian, Nicola Bezzo, Luca Carlone, Venanzio Cichella, Jia Deng, Nima Fazeli, Maani Ghaffari-Jadidi; second row, left to right: Grace Gu, Leonidas Guibas, Felix Heide, Ralph Hollis, Robert Katzschmann, Sven Koenig, George Konidaris; third row, left to right: Sergey Levine, Jennifer Lewis, Maja Matarić, Jan Peters, Lerrel Pinto, Robert Platt, Nancy Pollard; and bottom row, left to right: Alessandro Rizzo, Oren Salzman, Roland Siegwart, Pratap Tokekar, James Wang, Shenlong Wang, and Yuke Zhu are among the recipients from the Amazon Research Awards Fall 2021 call for proposals under the Robotics CFP.

Recipients have access to more than 300 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.

Top row, left to right:NAMES; second row, left to right:NAMES are among the recipients from the Amazon Research Awards Winter 2022 call for proposals under the Alexa: Fairness in AI CFP.
Top row, left to right:NAMES; second row, left to right:NAMES are among the recipients from the Amazon Research Awards Winter 2022 call for proposals under the Alexa: Fairness in AI CFP.

"Research in automated reasoning is deeply intertwined with a broad range of other research areas, touching machine learning, hardware and software engineering, robotics, and life sciences," said Daniel Kroening, an Automated Reasoning Group senior principal scientist. "The 2021 Amazon Research Awards reflect this breadth, and the interdisciplinary nature of research that is necessary to take computing one step closer to that magic spark that drives human reasoning."

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.

The table below lists, in alphabetical order, Fall 2021 cycle call-for-proposal recipients.

RecipientUniversityResearch title
Aws AlbarghouthiUniversity of Wisconsin-MadisonTeaching SMT Solvers Probability Theory
Nada AminHarvard UniversityExtensible Models and Proofs
Nora AyanianBrown UniversityLarge-Scale Labeled Multi-Agent Pathfinding for Warehouses
Clark BarrettStanford UniversityHydraScale: Solving SMT Queries in the Serverless Cloud
Ivan BeschastnikhUniversity of British ColumbiaCompiling Distributed System Models into Implementations
Nicola BezzoUniversity of VirginiaTowards Safe and Agile Robot Navigation in Occluding and Dynamic Environments
William BowmanUniversity of British ColumbiaStatic reasoning for memory in compilers and intermediate languages
Yinzhi CaoJohns Hopkins UniversityAutomatic Static Resource Analysis for Serverless Computing
Luca CarloneMassachusetts Institute of TechnologyReal-time Spatial AI for Robotics
Trevor CarlsonNational University of SingaporeAccelerating SAT Solving with a Flexible FPGA-Programming Platform
Marsha ChechikUniversity Of TorontoUnsatisfiability Proofs for Monotonic Theories
Venanzio CichellaUniversity Of IowaConcurrent allocation and planning for large-scale multi-robot systems
Cas CremersCISPA Helmholtz Center for Information SecurityKeyLife: Automated Formal Analysis for Key Lifecycles in Security Protocols with Policies, Delegation, and Compromise
Elizabeth CroftMonash UniversityHelp me!: Humans supporting robots through Augmented Reality
Jia DengPrinceton UniversityOptimization-Inspired Neural Networks for Visual SLAM
Derek DreyerMPI - SWSRefinedRust: Automating the Verification of Rust Programs in the Presence of Unsafe Code
Tudor DumitrasUniversity of Maryland, College ParkMitigating the impact of behavior variability and label noise on ML-based malware detectors
Nima FazeliUniversity of MichiganObject Manipulation with High-Resolution Tactile Sensors
Earlence FernandesUniversity of Wisconsin-MadisonVerifiable Distributed Computation
Marcelo FriasBuenos Aires Institute of TechnologyModular Bounded Verification with Expressive Contracts
Sicun GaoUniversity of California, San DiegoInterior Search Methods in SMT
Maani Ghaffari-JadidiUniversity of MichiganRobust low-cost dead reckoning and localization for home robotics using invariant state estimation
Roberto GiacobazziUniversity of VeronaImplicit program analysis
Ronghui GuColumbia UniversityLearning Inductive Invariants for Real Distributed Protocols
Grace GuUniversity of California, BerkeleyDeep learning-enabled robust grasping for pneumatic actuators
Leonidas GuibasStanford UniversityGeneralPurpose 3D Perception of Object Functionality
Arie GurfinkelUniversity of WaterlooFormal Proofs for Trusted Execution Environments
Hamed HaddadiImperial College LondonAuditable Model Privacy using TEEs
Felix HeidePrinceton UniversityInverse Neural Rendering
Ralph HollisCarnegie Mellon UniversityLow Cost Dynamic Mobile Robots for Research and Teaching
Hongxin HuSUNY, BuffaloExplaining Learning-based Intrusion Detection Systems for Active Intrusion Responses
Jean-Baptiste JeanninUniversity of Michigan-Ann ArborAutomatic Verification of Distributed Systems Implementations
Robert KatzschmannETH ZurichDesign and Control Optimization of Soft Gripper Mechanisms for Manipulation
Anirudh Sivaraman KaushalramNew York UniversityObserving and controlling microservice deployments
Steve KoSimon Fraser UniversityPractical Symbolic Execution for Rust
Sven KoenigUniversity of Southern CaliforniaHybrid Search- and Traffic-Based MAPF Systems for Fulfillment Centers
George KonidarisBrown UniversityLearning Composable Manipulation Skills
Emmanuel LetouzéPompeu Fabra UniversityLeveraging Digital Data for Monitoring Human Rights and Social Dynamics Along and Around Value Chains
Sergey LevineUniversity of California, BerkeleyRobotic Learning with Reusable Data
Jennifer LewisHarvard UniversityComputational Co-Design of Dexterous Rigid-Soft Grippers With Intrinsic Tactile-Sensing-Based Control
Maja MatarićUniversity of Southern CaliforniaLearning User Preferences for In-Home Robots Through In Situ Augmented Reality
James NobleVictoria University Of Wellington“Programming Made Hard” Made Easier: Improving Dafny’s Human Factors
Rohan PadhyeCarnegie Mellon UniversityCoverage-Guided Property-Based Testing of Concurrent Programs
Jan PetersTU DarmstadtLearning Robot Manipulation from Tactile Feedback
Lerrel PintoNew York UniversityVisual Imitation in the Wild through Decoupled Representation Learning
Robert PlattNortheastern UniversityOn-robot manipulation learning via equivariant models
Nancy PollardCarnegie MellonContact Areas for Manipulation Capture, Retargeting, and Hand Design
Pavithra PrabhakarKansas State UniversityConformance Checking of Evolving ML Software Systems
Francesco RanzatoUniversity of VeronaImplicit program analysis
Sanjay RaoPurdue UniversityAnswering counterfactuals from offline data for video streaming
Bruno RibeiroPurdue UniversityAnswering counterfactuals from offline data for video streaming
Talia RingerUniversity of Illinois Urbana-ChampaignNeurosymbolic Proof Synthesis & Repair
Alessandro RizzoPolitecnico di TorinoPhysics-Informed Machine Learning for Trustworthy Control of Autonomous Robots
Camilo RochaPontificia Universidad Javeriana CaliProbabilistic and Symbolic Tools for P Program Verification
Andrei SabelfeldChalmers University of TechnologyDeepCrawl: Automated Reasoning for Deep Web Crawling
Oren SalzmanTechnion - Israel Institute of TechnologyIncreasing throughput in automated warehouses via environment manipulation
Ilya SergeyNational University of SingaporeScaling Automated Verification of Distributed Protocols with Specification Transformation and Synthesis
Michele SevegnaniUniversity of GlasgowFrom Whiteboards to Models: Diagrammatic Formal Modelling for Everyone
Roland SiegwartETH ZurichAutonomous Navigation of Aerial Robotic Manipulators in Unstructured Indoor and Outdoor Environments
Ramesh SitaramanUniversity of Massachusetts AmherstDesign and Evaluation of ABR Algorithms for High-Performance Video Delivery
Fu SongShanghaiTech UniversityEfficient and Precise Verification for Constant-Time and Time-Balancing of Cryptosystems
Zhendong SuETH ZurichPractical Techniques for Reliable, Robust and Performant SMT Solvers
Jiliang TangMichigan State UniversityTaming Graph Anomaly Detection via Graph Neural Networks
Pratap TokekarUniversity of Maryland, College ParkMulti-Robot Coordination through the Lens of Risk
Daniel VarroMcGill UniversityGraph Solver as a Service
Yakir VizelTechnion - Israel Institute of TechnologyQuantified Invariants
David WagnerUniversity of California, BerkeleyMachine Learning for Malware Detection: Robustness against Concept Drift
James WangPennsylvania State UniversityAffective and Social Interaction between Human and Intelligent Machine in Daily Activities
Shenlong WangUniversity of Illinois Urbana-ChampaignSafely Test Autonomous Vehicles with Augmented Reality
Thomas WiesNew York UniversityA Modular Library of Verified Concurrent Search Structure Algorithms
Anton WijsEindhoven University of TechnologyMany-Core Acceleration of State Space Construction and Analysis
Xinyu XingNorthwestern UniversityBattling Noisy-label Classification
Meng XuUniversity Of WaterlooFinding Specification Blind Spots with Fuzz Testing
Yuke ZhuUniversity of Texas at AustinInteractive Learning Framework for Building Structured Object Models from Play
Andrew ZissermanUniversity of OxfordAudio-Visual Synchronisation for General Videos

Related content

US, WA, Seattle
AWS Infrastructure Services owns the design, planning, delivery, and operation of all AWS global infrastructure. In other words, we’re the people who keep the cloud running. We support all AWS data centers and all of the servers, storage, networking, power, and cooling equipment that ensure our customers have continual access to the innovation they rely on. We work on the most challenging problems, with thousands of variables impacting the supply chain — and we’re looking for talented people who want to help. You’ll join a diverse team of software, hardware, and network engineers, supply chain specialists, security experts, operations managers, and other vital roles. You’ll collaborate with people across AWS to help us deliver the highest standards for safety and security while providing seemingly infinite capacity at the lowest possible cost for our customers. And you’ll experience an inclusive culture that welcomes bold ideas and empowers you to own them to completion. Come work for M13 - an AWS team specializing in the deception and disruption of cyber threats. We are looking for an Applied Scientist who is passionate about the security domain. You will build services and tools for security engineers and developers that leverage artificial intelligence and machine learning to pull unique insights about the cyber threat landscape. You will be part of a team building Large Language Model (LLM)-based services with the focus on enabling AWS teams to interact with our threat data. The team works in close collaboration with other AWS security services to power mitigations that protect the global AWS network and features in external security services such as Amazon GuardDuty, AWS WAF, and AWS Network Firewall. If you are excited about combating the ever evolving threat landscape then we'd love to talk to you. As an Applied Scientist, you are recognized for your expertise, advise team members on a range of machine learning topics, and work closely with software engineers to drive the delivery of end-to-end modeling solutions. Your work focuses on ambiguous problem areas where the business problem or opportunity may not yet be defined. The problems that you take on require scientific breakthroughs. You take a long-term view of the business objectives, product roadmaps, technologies, and how they should evolve. You drive mindful discussions with customers, engineers, and scientist peers. You bring perspective and provide context for current technology choices, and make recommendations on the right modeling and component design approach to achieve the desired customer experience and business outcome. Key job responsibilities • Understand the challenges that security engineers and developers face when building software today, and develop generalizable solutions. • Collaborate with the team to pave the way towards bringing your solution into production systems. Lead cross team projects and ensure technical blockers are resolved • Communicate and document your research via publishing papers in external scientific venues. About the team *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. *Diverse Experiences* Amazon values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. *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. *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 and 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.
US, WA, Seattle
We are seeking a senior scientist with demonstrated experience in A/B testing along with related experience with observational causal modeling (e.g. synthetic controls, causal matrix completion). Our team owns "causal inference as a service" for the Pricing and Promotions organization; we run A/B tests on new pricing, promotions, and pricing/promotions CX algorithms and, where experimentation is impractical, conduct observational causal studies. Key job responsibilities We are seeking a senior scientist to help envision, design, and build the next generation of pricing, promotions, and pricing/promotions CX for Amazon. On our team, you will work at the intersection of economic theory, statistical inference, and machine learning to design and implement in production new statistical methods for measuring causal effects of an extensive array of business policies. This position is perfect for someone who has a deep and broad analytic background, is passionate about using mathematical modeling and statistical analysis to make a real difference. You should be familiar with modern tools for data science and business analysis and have experience coding with engineers to put projects into production. We are particularly interested in candidates with research background in experimental statistics. A day in the life - Discuss with business problems with business partners, product managers, and tech leaders - Brainstorm with other scientists to design the right model for the problem at hand - Present the results and new ideas for existing or forward looking problems to leadership - Dive deep into the data - Build working prototypes of models - Work with engineers to implement prototypes in production - Analyze the results and review with partners About the team We are a team of scientists who design and implement the econometrics powering pricing, promotions, and pricing/promotions CX.
US, WA, Seattle
Do you want to join a team of innovative scientists to research and develop generative AI technology that would disrupt the industry? Do you enjoy dealing with ambiguity and working on hard problems in a fast-paced environment? Amazon Connect is a highly disruptive cloud-based contact center from AWS that enables businesses to deliver intelligent, engaging, dynamic, and personalized customer service experiences. As an Applied Scientist on our team, you will work closely with senior technical and business leaders from within the team and across AWS. You distill insight from huge data sets, conduct cutting edge research, foster ML models from conception to deployment. You have deep expertise in machine learning and deep learning broadly, and extensive domain knowledge in natural language processing, generative AI and LLMs, etc. The ideal candidate has the ability to understand, implement, innovate and on the state-of-the-art generative AI based systems. You are comfortable with quickly prototyping and iterating your ideas to build robust ML models using technology such as PyTorch, Tensorflow, AWS Sagemaker, and SparkML. Our team is at an early stage, so you will have significant impact on our ML deliverables with little operational load from existing models/systems. We have a rapidly growing customer base and an exciting charter in front of us that includes solving highly complex engineering and scientific problems. We are looking for passionate, talented, and experienced people to join us to innovate on modern contact centers in the cloud. The position represents a rare opportunity to be a part of a fast-growing business soon after launch, and help shape the technology and product as we grow. You will be playing a crucial role in developing the next generation contact center, and get the opportunity to design and deliver scalable, resilient systems while maintaining a constant customer focus. Our team is leading ML and optimization features in Amazon Connect. We are a team of scientists and engineers working on multiple science projects for Amazon Connect. We use state-of-the-art science and engineering practices to address the hard problems in contact center operation and management for our customers, and we move fast to implement solutions and refine them based on customer feedback. Learn more about Amazon Connect here: https://aws.amazon.com/connect/ 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. 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.
US, CA, San Diego
Do you want to join an innovative team of scientists who use machine learning and statistical techniques to help Amazon provide the best customer experience by preventing eCommerce fraud? Are you excited by the prospect of analyzing and modeling terabytes of data and creating state-of-the-art algorithms to solve real world problems? Do you like to own end-to-end business problems/metrics and directly impact the profitability of the company? Do you enjoy collaborating in a diverse team environment? If yes, then you may be a great fit to join the Amazon Buyer Risk Prevention (BRP) Machine Learning group. We are looking for a talented scientist who is passionate to build advanced algorithmic systems that help manage safety of millions of transactions every day. Key job responsibilities Use machine learning and statistical techniques to create scalable risk management systems Learning and understanding large amounts of Amazon’s historical business data for specific instances of risk or broader risk trends Design, development and evaluation of highly innovative models for risk management Working closely with software engineering teams to drive real-time model implementations and new feature creations Working closely with operations staff to optimize risk management operations, Establishing scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation Tracking general business activity and providing clear, compelling management reporting on a regular basis Research and implement novel machine learning and statistical approaches
US, WA, Seattle
Amazon’s Global Media and Entertainment (GME) organization is creating a future of entertainment where creative content, innovation, and commerce come together. We leverage Amazon’s unique expertise across video, music, gaming, and more to create a truly immersive entertainment experience. Our team, GME Science, is focused on building science tools to optimize Amazon’s entertainment offerings, so that we can provide a great customer experience while operating as a sustainable and profitable business. We push ourselves to Think Big, building ambitious models that create value in multiple GME businesses. This role will expand our team’s measurement work. Business leaders need to quickly understand the long-term impact of various investments, such as new website features, content creation, or marketing campaigns. Our team figures out how to take short-term signals – such as clicks or signups – and turn them into estimates of long-term financial impacts. We work with measurement teams in each business as well as central teams to build foundational measurement science and adapt it for unique use cases. One particular application for this role is to build a principled approach to valuing content/talent deals that include multiple GME businesses. Each deal is unique, featuring talent from film, sports, music, and other top industries, with contract terms that could include video content, podcasts, live appearances, and more. Our valuations need to be structured so that they are comparable across deals, yet flexible enough to account for diverse contracts. To be successful in this role, you will need effective communication, an ability to work closely with stakeholders across our many GME partner teams, and the skill to translate data-driven findings into actionable insights. This includes developing a deep understanding of our business context, which is ambiguous and can change quickly. Your work will be used by decision-makers across GME to deliver the best entertainment experience for our customers, which means we have a high bar. Our healthy team culture is supportive and fast-paced, and we prioritize learning, growth, and helping each other to continuously raise the bar. Impact and Career Growth In today’s entertainment landscape, critical decisions are made with data and economic models. You’ll help GME leaders ask the right questions, and then deliver data-driven answers, creating the future of GME at Amazon. You’ll help define a long-term science vision in this space and translate it into an actionable roadmap. This role combines science leadership, organizational ability, technical strength, product focus, and business understanding – a perfect recipe for career growth as an economist in tech. Key job responsibilities • Design and build econometric models, especially causal models, to measure the value of the business and its many features • Develop science products from concept to prototype to production, incorporating feedback from scientists and business partners • Independently identify and pursue new opportunities to leverage economic insights across GME businesses • Write business and technical documents communicating business context, methods, and results to business leadership and other scientists • Serve as a technical reviewer for our team and related teams, including document and code reviews
GB, Cambridge
The Artificial General Intelligence team (AGI) has an exciting position for an Applied Scientist with a strong background NLP and Large Language Models to help us develop state-of-the-art conversational systems. As part of this team, you will collaborate with talented scientists and software engineers to enable conversational assistants capabilities to support the use of external tools and sources of information, and develop novel reasoning capabilities to revolutionise the user experience for millions of Alexa customers. Key job responsibilities As an Applied Scientist, you will develop innovative solutions to complex problems to extend the functionalities of conversational assistants . You will use your technical expertise to research and implement novel algorithms and modelling solutions in collaboration with other scientists and engineers. You will analyse customer behaviours and define metrics to enable the identification of actionable insights and measure improvements in customer experience. You will communicate results and insights to both technical and non-technical audiences through written reports, presentations and external publications.
US, WA, Seattle
Amazons Price Optimization science team is seeking a Senior Scientist to harness planet scale multi-modal datasets, navigate a continuously evolving competitor landscape, in order to regularly generate fresh customer-relevant prices on billions of Amazon and Third Party Seller products worldwide. This is a high visibility, high impact role for a seasoned, intellectually curious scientist able to partition customer problems into discrete solvable components, build or leverage existing approaches to deliver those components, and innovate to deploy the science into measurable customer-improving outputs. This role requires an individual with exceptional machine learning and reinforcement learning modeling expertise, a strong statistical background, excellent cross-functional collaboration skills, outstanding business acumen, and an entrepreneurial spirit. We are looking for an experienced innovator, who is a self-starter, comfortable with ambiguity, demonstrates strong attention to detail, and has the ability to work in a fast-paced and ever-changing environment. Price is a highly relevant input into many partner-team architectures, and is highly relevant to the customer, therefore this role creates the opportunity to drive extremely large impact (measured in Bs not Ms), but demands careful thought and clear communication. Key job responsibilities We are hiring a senior applied scientist to drive our pricing optimization initiatives. The Price Optimization science team drives cross-domain and cross-system improvements through: * Using cross-ASIN signals to optimally price bundles, ensure price rationality across products, and discovering and launch optimal promotional bundles * invent and deliver price optimization, simulation, and competitiveness tools for 3p Sellers. * shape and extend our bandit optimization platform - a pricing centric multi-armed bandit platform that automates the optimization of various system parameters and price inputs. * Promotion optimization initiatives exploring CX, discount amount, and cross-product optimization opportunities. * Identifying opportunities to optimally price across systems and contexts (marketplaces, request types, event periods) About the team The Pricing Optimization science team owns price quality, discovery and discount optimization initiatives across Amazon’s internal pricing architecture as well as upwards into the customer discovery funnel. We leverage planet scale data on billions of Amazon and external competitor products to build advanced optimization models for pricing, elasticity estimation, product substitutability, and optimization. We preserve long term customer trust by ensuring Amazon's prices are always competitive and error free.
US, CA, Sunnyvale
The Artificial General Intelligence (AGI) team is looking for a highly skilled and experienced Senior Applied Scientist, to lead the development and implementation of cutting-edge algorithms and models for supervised fine-tuning and reinforcement learning through human feedback; with a focus across text, image, and video modalities. As a Senior Applied Scientist, you will play a critical role in driving the development of Generative AI (GenAI) technologies that can handle Amazon-scale use cases and have a significant impact on our customers' experiences. Key job responsibilities - Collaborate with cross-functional teams of engineers, product managers, and scientists to identify and solve complex problems in GenAI - 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 GenAI 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
US, WA, Bellevue
Amazon’s Last Mile Team is looking for a passionate individual with strong optimization and analytical skills to join its Last Mile Science team in the endeavor of designing and improving the most complex planning of delivery network in the world. Last Mile builds global solutions that enable Amazon to attract an elastic supply of drivers, companies, and assets needed to deliver Amazon's and other shippers' volumes at the lowest cost and with the best customer delivery experience. Last Mile Science team owns the core decision models in the space of jurisdiction planning, delivery channel and modes network design, capacity planning for on the road and at delivery stations, routing inputs estimation and optimization. Our research has direct impact on customer experience, driver and station associate experience, Delivery Service Partner (DSP)’s success and the sustainable growth of Amazon. Optimizing the last mile delivery requires deep understanding of transportation, supply chain management, pricing strategies and forecasting. Only through innovative and strategic thinking, we will make the right capital investments in technology, assets and infrastructures that allows for long-term success. Our team members have an opportunity to be on the forefront of supply chain thought leadership by working on some of the most difficult problems in the industry with some of the best product managers, scientists, and software engineers in the industry. Key job responsibilities Candidates will be responsible for developing solutions to better manage and optimize delivery capacity in the last mile network. The successful candidate should have solid research experience in one or more technical areas of Operations Research or Machine Learning. These positions will focus on identifying and analyzing opportunities to improve existing algorithms and also on optimizing the system policies across the management of external delivery service providers and internal planning strategies. They require superior logical thinkers who are able to quickly approach large ambiguous problems, turn high-level business requirements into mathematical models, identify the right solution approach, and contribute to the software development for production systems. To support their proposals, candidates should be able to independently mine and analyze data, and be able to use any necessary programming and statistical analysis software to do so. Successful candidates must thrive in fast-paced environments, which encourage collaborative and creative problem solving, be able to measure and estimate risks, constructively critique peer research, and align research focuses with the Amazon's strategic needs.
IN, KA, Bangalore
Alexa is the voice activated digital assistant powering devices like Amazon Echo, Echo Dot, Echo Show, and Fire TV, which are at the forefront of this latest technology wave. To preserve our customers’ experience and trust, the Alexa Sensitive Content Intelligence (ASCI) team creates policies and builds services and tools through Machine Learning techniques to detect and mitigate sensitive content across Alexa. We are looking for an experienced Applied Science Manager to lead a team to build industry-leading technologies in attribute extraction and sensitive content detection across all languages and countries. A Manager, Applied Science will be a tech leader for a team of exceptional scientists to develop novel algorithms and modeling techniques to advance the state of the art in NLP or CV related tasks. You will work in a hybrid, fast-paced organization where scientists, engineers, and product managers work together to build customer facing experiences. You will collaborate with and mentor other scientists to raise the bar of scientific research in Amazon. Your work will directly impact our customers in the form of products and services that make use of speech, language, and computer vision technologies. We are looking for a leader with strong technical experiences a passion for building scientific driven solutions in a fast-paced environment. You should have good understanding of NLP models (e.g. LSTM, transformer based models) or CV models (e.g. CNN, AlexNet, ResNet) and where to apply them in different business cases. You leverage your exceptional technical expertise, a sound understanding of the fundamentals of Computer Science, and practical experience of building large-scale distributed systems to creating reliable, scalable, and high-performance products. In addition to technical depth, you must possess exceptional communication skills and understand how to influence key stakeholders. You will be joining a select group of people making history producing one of the most highly rated products in Amazon's history, so if you are looking for a challenging and innovative role where you can solve important problems while growing as a leader, this may be the place for you. Key job responsibilities You'll lead and manage the science driven solution development including design, run experiments, research new algorithms, and find new ways of optimizing customer experience. You set examples for the team on good science practice and standards. Besides theoretical analysis and innovation, you will work closely with talented engineers and ML scientists to put your algorithms and models into practice. Your work will directly impact the trust customers place in Alexa, globally. You contribute directly to our growth by hiring smart and motivated Scientists to establish teams that can deliver swiftly and predictably, adjusting in an agile fashion to deliver what our customers need. A day in the life You will be working with a group of talented scientists as well as stakeholder from different functional areas (e.g. product, engineering) on researching algorithm and running experiments to test scientific proposal/solutions to improve our sensitive contents detection and mitigation. This will involve collaboration with partner teams including engineering, PMs, data annotators, and other scientists to discuss data quality, policy, and model development. You will mentor other scientists, review and guide their work, help develop roadmaps for the team. You work closely with partner teams across Alexa to deliver platform features that require cross-team leadership. About the team The mission of the Alexa Sensitive Content Intelligence (ASCI) team is to (1) minimize negative surprises to customers caused by sensitive content, (2) detect and prevent potential brand-damaging interactions, and (3) build customer trust through appropriate interactions on sensitive topics. The term “sensitive content” includes within its scope a wide range of categories of content such as offensive content (e.g., hate speech, racist speech), profanity, content that is suitable only for certain age groups, politically polarizing content, and religiously polarizing content. The term “content” refers to any material that is exposed to customers by Alexa (including both 1P and 3P experiences) and includes text, speech, audio, and video.