Social

63 Amazon Research Award recipients announced

Awardees, who represent 41 universities in 8 countries, have access to Amazon public datasets, along with AWS AI/ML services and tools.

By Amazon Research Awards team
November 25, 2025
8 min read
Share

Amazon Research Awards (ARA) provides unrestricted funds and AWS Promotional Credits to academic researchers investigating various research topics in multiple disciplines. This cycle, ARA received many excellent research proposals from across the world and today is publicly announcing 63 award recipients who represent 41 universities in 8 countries.

This announcement includes awards funded under five call for proposals during the spring 2025 cycle: AI for Information Security, Amazon Ads, AWS AI: Agentic AI, Build on Trainium and Think Big. Proposals were reviewed for the quality of their scientific content and their potential to impact both the research community and society. 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.

Recipients have access to more than 700 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.

Rule generation subgraph
How Amazon uses AI agents to anticipate and counter cyber threats
Amazon's competitive-agent architecture creates a continuous improvement cycle that develops security protections at machine speed, reducing what typically takes weeks down to hours.

"Amazon Research Awards are enabling incredibly impactful work to improve human health—from revolutionizing and democratizing structural biology tools, which can accelerate discovery of candidate molecules for new drugs to help patients, to predicting the etiology of a stroke in order to start the appropriate therapies, or interpreting digital phenotyping data to help with mental health services," said Christine Silvers, AWS Principal Healthcare Advisor. "These are just three examples of projects that recipients have received Amazon Research Awards for. The potential for improving healthcare amongst all of the spring 2025 plus past and future awardees is staggering and inspiring.“

"Academic AI researchers face a fundamental challenge: advancing machine learning research and educating the next generation requires access to cutting-edge infrastructure that's both powerful and affordable," said Yida Wang, AWS AI Principal Applied Scientist. "The Build on Trainium program directly addresses this barrier. We are working with leading AI research universities such as, UC Berkeley, Stanford, CMU, MIT, UIUC, UCLA, and many others.  At CMU, researchers achieved significant improvements over state-of-the-art FlashAttention in just one week. At MIT, researchers trained 3D medical imaging models with 50% higher throughput and lower cost, reducing training time from months to weeks. Build on Trainium represents AWS's commitment to democratizing AI research through collaborative partnership with academia—fostering an environment where researchers experiment freely, students learn on production-scale infrastructure, and academic innovations shape the future of machine learning for everyone."

The tables below list, in alphabetical order by last name, the spring 2025 cycle call-for-proposal recipients, sorted by research area.

AI for Information Security

ARA-AIInfoSecurity-1200x750-02.png

Recipient

University

Research title

Christopher Fletcher

University Of California, Berkeley

Design and Verification of High-Assurance Key Management Services for Stateful Confidential Computing

Zhou Li

University Of California, Irvine

Precise and Analyst-friendly Attack Provenance on Audit Logs with LLM

Yu Meng

University of Virginia

Weakly-Supervised RLHF: Modeling Ambiguity and Uncertainty in Human Preferences

Jelena Mirkovic

University of Southern California

Safe and Secure API Discovery for Agentic AI

Aanjhan Ranganathan

Northeastern University

Understanding How LLMs Hack: Interpretable Vulnerability Detection and Remediation

Sanjit Seshia

University Of California, Berkeley

Design and Verification of High-Assurance Key Management Services for Stateful Confidential Computing

Alexey Tregubov

University of Southern California

Safe and Secure API Discovery for Agentic AI

Ziming Zhao

Northeastern University

Understanding How LLMs Hack: Interpretable Vulnerability Detection and Remediation

Amazon Ads

ARA Spring 2025 recipients

Recipient

University

Research title

Xiaojing Liao

University of Illinois at Urbana–Champaign

Adversarial Misuse of Large Language Models in Digital Advertising: Benchmarking and Mitigation

Tianhao Wang

University of Virginia

Adversarial Misuse of Large Language Models in Digital Advertising: Benchmarking and Mitigation

AWS Agentic AI

ARA Spring 2025 recipients

Recipient

University

Research title

Faez Ahmed

Massachusetts Institute of Technology

AutoDA-Sim: A Multi-Agent Framework for Safe, Aesthetic, and Aerodynamic Vehicle Design

Fabio Anza

University of Maryland, Baltimore County

Physics Co-Pilot: An LLM-Orchestrated Scientific Assistant for Physics Research

Andrea Bajcsy

Carnegie Mellon University

Fine Grained Planning Evaluation for VLM Web Agents

Niranjan Balasubramanian

Stony Brook University

Efficient and Effective Long-Horizon Reasoning for Interactive LLM Agents

Andreea Bobu

Massachusetts Institute of Technology

Contextual Harm Mitigation and Automated Backtracking in Computer Use Agents

Joseph Campbell

Purdue University, West Lafayette

Open-World Probabilistic Theory of Mind

Cong Chen

Dartmouth College

Empowering Power Systems and Market Operations with Behavioral Generative Agents

Chunyang Chen

Technical University of Munich

Functional Bug-Aware Software Testing via Intelligent Computer Use Agents

Shay Cohen

University of Edinburgh

Diffusion-inspired chain-of-thought self-revision

Fernando De la Torre

Carnegie Mellon University

Fine Grained Planning Evaluation for VLM Web Agents

Sidong Feng

Monash University

Functional Bug-Aware Software Testing via Intelligent Computer Use Agents

James Fogarty

University of Washington, Seattle

Leveraging Multiple Representations in Multi-Agent Mobile App Interface Understanding and Task Execution

Surbhi Goel

University of Pennsylvania

Efficient and Safe Protocols for Collaborative Agentic AI

Nika Haghtalab

University of California, Berkeley

Multi-Agent AI Alignment

Irwin King

The Chinese University of Hong Kong

WebAGI: VLM-Driven Framework for Robust Web Automation and Planning in Agentic AI

Emma Lejeune

Boston University

Formidable yet Solvable: Scientific Computing Tasks for Agentic AI

Bang Liu

University of Montreal

Foundation Agents and Protocol for Collaborative Agentic AI

Harsha Madhyastha

University of Southern California

Improving the Efficiency of Web Agents

Michael Macy

Cornell University

Artificial Collective Intelligence: The Structure and Dynamics of LLM Communities

Radu Marculescu

University of Texas at Austin

Collaborative Continual Learning in Multimodal Multi-Agent Systems

Lianhui Qin

University of California, San Diego

ReaL-Agent: A Retrieval-and-Reasoning Agent for Deep, Cross-Modality Retrieval

Mahnam Saeednia

Delft University of Technology

Heterogeneous Multi-Agent Collaboration For Built-in Resilience

Maarten Sap

Carnegie Mellon University

OpenAgentSafety: Measuring and Mitigating Safety Harms of LLM-based AI Agent Interactions

Vitaly Shmatikov

Cornell University

Contextual Security for Multi-Agent Systems

Haim Sompolinsky

Harvard University

Lifelong learning in agentic AI through gated memory modules

John Torous

Harvard University

Interpreting Digital Phenotyping Data with LLM-Based Agentic Assistants for Mental Health Services

Jindong Wang

College of William & Mary

Structure Matters: Task-Optimized Topologies for LLM Agents

Xiaolong Wang

University of California, San Diego

Agentic World Representation

Zhi-Li Zhang

University of Minnesota, Twin Cities

NetGenius: Agentic AI for Next-Generation Wireless Network Autonomous Configurations and Intelligent Operations

Jiawei Zhou

Stony Brook University

Efficient and effective long-horizon reasoning for interactive LLM agents

Build on Trainium

ARA Spring 2025 recipients

Recipient

University

Research title

Saikat Dutta

Cornell University

VERA: Automated Testing for Improving the Reliability of Neuron Compiler Toolchain

Kuan Fang

Cornell University

Fast Adaptation of Multi-Modal Foundation Models for Robotic Perception and Control

Shizhong Han

Lieber Institute for Brain Development

Optimizing and scaling pretraining and preference-based fine-tuning of Large Chemical Models

Sitao Huang

University of California, Irvine

Automatic Kernel Synthesis and Tuning for AWS Trainium via Profile-Guided Graph Topology Optimization

Wataru Kameyama

Waseda University

Accelerating Vision-Language Autonomous Driving with AWS Trainium

Dong Li

University of California, Merced

Efficient Sparse Training with Adaptive Expert Parallelism on AWS Trainium

Xiaoxiao Li

University of British Columbia

Efficient MoE LLMs via Pruning and Matryoshka Quantization on AWS Trainium

Jiang Liu

Waseda University

Accelerating Vision-Language Autonomous Driving with AWS Trainium

Xiaoyi Lu

University of California, Merced

Accelerating Large Language and Reasoning Model Workloads with AWS Trainium

Satoshi Masuda

Tokyo City University

LLM for Software Modeling Brain in Multi Language

Andrew McCallum

University of Massachusetts, Amherst

Overcoming Fundamental Reasoning Limitations of LLMs by Always Thinking before Writing

Xupeng Miao

Purdue University, West Lafayette

Towards Communication-Efficient Distributed Training of Large Foundation Models by Dataflow-aware Optimizations

Michael Nagle

Lieber Institute for Brain Development

Optimizing and scaling pretraining and preference-based fine-tuning of Large Chemical Models

Jean-Christophe Nebel

Kingston University London

Efficient Architectures for Genomic Variant Interpretation: Language Models for Non-Coding DNA Variant Analysis

Farzana Rahman

Kingston University London

Efficient Architectures for Genomic Variant Interpretation: Language Models for Non-Coding DNA Variant Analysis

Rohan Sachdeva

University of California, Berkeley

Learning Host–Microbial Genetic Element Interactions with Genomic Language Models

Yanning Shen

University of California, Irvine

Automatic Kernel Synthesis and Tuning for AWS Trainium via Profile-Guided Graph Topology Optimization

Yun Song

University of California, Berkeley

Learning Host–Microbial Genetic Element Interactions with Genomic Language Models

Hoa Vo

Indiana University Bloomington

AI-Powered Travel Pattern Detection in VR for Occupant Behavior Analysis Using AWS Trainium

Minjia Zhang

University of Illinois Urbana-Champaign

Trainium-native MoE: Developing kernel and system optimizations for efficient and scalable MoE training

Think Big

ARA Spring 2025 recipients

Recipient

University

Research title

Tianlong Chen

University of North Carolina at Chapel Hill

Leveraging Molecular Dynamics to Empower Protein AI Models

William H. Lee

Yale School of Medicine

AI-powered prediction of ischemic stroke etiologies using multi-modal data

Piotr Sliz

Harvard Medical School

SBCloud – A Transformative Model for Scalable Structural Biology Research
Tags
About the Author
Amazon Research Awards team

Related content

Work with us

See more jobs See more jobs
Applied Scientist, Stores Economics & Science (SEAS)
US, WA, Seattle
Stores Economics and Science (SEAS) is an interdisciplinary science and engineering team in Amazon's Stores organization with a peak-jumping mission: we apply expertise in science and engineering to move from local to global optima in methods, models, and software. We pursue this mission by leveraging frontier science; collaborating with partner teams; and learning from the tools, experience, and perspective of others. We scale by solving problems, first in the small to prove concepts, and then in the large by building scalable solutions. We also help other teams within Amazon scale by hiring and developing the best and embedding them in other business units. In 2026, we are focused on economics and science in areas related to (1) lowering cost-to-serve, (2) optimizing selection, and (3) emerging machine learning. We also have some ongoing and highly-leveraged collaborations that help partner teams inside Amazon short-circuit months of R&D or otherwise look around corners. We are looking for an Applied Scientist to build and deliver state-of-the-art science and engineering solutions to improve our Stores business. In this role, you will work in a team of scientists and engineers with backgrounds in machine learning, NLP, IR, statistics, and economics to identify bottlenecks in our business, conceive new ideas to overcome those challenges, and deploy scientific solutions in partnership with product teams. Your responsibilities include developing and maintaining the scientific models, benchmarks, and services. Graduate education or hands-on experience in machine learning, optimization, causal inference, Bayesian statistics, deep learning, or other quantitative scientific fields is a big plus. To be successful in this role, you should be a quick learner and comfortable with a high degree of ambiguity. Key job responsibilities The successful candidate will lead large-scale science initiatives from research to production and translate complex business problems into mathematical frameworks. They will design and implement large-scale algorithms for complex supply chain and marketplace problems, and design incentive-compatible mechanisms for marketplace challenges. The ideal candidate will have a strong publication record in top-tier conferences/journals (INFORMS, EC, WINE, ICML, NeurIPS, etc.) and experience coordinating cross-functional projects. Hands-on experience building science solutions to mechanism design problems (e.g., optimal auction design, welfare maximization under constraints, incentive compatible coordination), with expertise in statistical learning and algorithm development. Leadership responsibilities include influencing technical strategy and roadmaps for complex initiatives, influencing senior stakeholders and shaping technical direction, and fostering team growth.
Applied Scientist, AWS Central Economics and Science
US, CA, San Francisco
AWS is one of Amazon’s largest and fastest growing businesses, serving millions of customers in more than 190 countries. We use cloud computing to reshape the way global enterprises use information technology. We are looking for entrepreneurial, analytical, creative, flexible leaders to help us redefine the information technology industry. If you want to join a fast-paced, innovative team that is making history, this is the place for you. AWS Central Economics & Science (ACES) drives best practices for objectively applying economics and science in decision making across AWS. The team collaborates with AWS science and business teams to identify, frame, and analyze complex and ambiguous problems of the highest priority. Through data-driven insights and modeling, ACES supports strategic decision-making across the AWS global organization, including sales operations and business performance optimization. The ACES Sales Channels team is hiring an Applied Scientist (Senior or below) to advance our mission of providing rigorous, causal-inference-driven recommendations for AWS sales optimization. This role will focus on building ML systems with a causal modeling foundation, designing seller incentive mechanisms, and developing intervention strategies across the entire sales motion. Key job responsibilities • Causal ML System Development: Build and deploy machine learning models that emphasize causal inference, ensuring recommendations are grounded in valid interventions • Incentive Design: Define and model incentives that drive desirable behaviors across AWS sales channels, partner programs, and reseller ecosystems • Stakeholder Collaboration: Work with business stakeholders to understand requirements, validate approaches, and ensure practical applicability of scientific solutions • Scientific Rigor: Promote findings at internal conferences and contribute to the team's reputation for methodological excellence A day in the life The ACES Sales Channels team works on understanding and optimizing AWS's sales channels, both direct (generalist and specialist sellers) and indirect (partners and Marketplace). Our work falls into three core areas: developing rigorous causal measurement and modeling frameworks using frontier economics and statistical methods; designing programs and incentives to improve customer and business outcomes; and building ML-based recommendation systems for sellers, partners, and other AWS stakeholders. 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. 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 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.
Applied Scientist II, Sponsored Products
US, NY, New York
The Sponsored Products and Brands team at Amazon Ads is re-imagining the advertising landscape through cutting-edge generative AI technologies, revolutionizing how millions of customers discover products and engage with brands across Amazon.com and beyond. We are at the forefront of re-inventing advertising experiences, bridging human creativity with artificial intelligence to transform every aspect of the advertising lifecycle from ad creation and optimization to performance analysis and customer insights. We are a passionate group of innovators dedicated to developing responsible and intelligent AI technologies that balance the needs of advertisers, enhance the shopping experience, and strengthen the marketplace. If you're energized by solving complex challenges and pushing the boundaries of what's possible with AI, join us in shaping the future of advertising. Key job responsibilities Participate in the Science hiring process as well as mentor other scientists - improving their skills, their knowledge of your solutions, and their ability to get things done. Identify and devise new video related solutions following a customer-obsessed scientific approach to address customer or business problems when the problem is ill-defined, needs to be framed, and new methodologies or paradigms need to be invented at the product level. Articulate potential scientific challenges of ongoing or future customers’ needs or business problems, and present interventions to address them. Independently assess alternative video related technologies, driving evaluation and adoption of those that fit best A day in the life As an Applied Scientist on the Sponsored Brands Video team, you will work with a team of talented and experienced engineers, scientists, and designers to help bring new products to market and ensure that our customers are delighted by what we create. The Sponsored Brands Video team is responsible for the design, development, and implementation of Sponsored Brands Video experiences worldwide. About the team The Sponsored Brands Video team within Sponsored Products and Brands creates relevant and engaging video experiences, connecting advertisers and shoppers. We are on a mission to make Amazon the best in class destination for shoppers to discover, engage and build affinity with brands, making shopping delightful, & personal.
Applied Scientist, Fauna
US, NY, New York
We are seeking an Applied Scientist to lead the development of evaluation frameworks and data collection protocols for robotic capabilities. In this role, you will focus on designing how we measure, stress-test, and improve robot behavior across a wide range of real-world tasks. Your work will play a critical role in shaping how policies are validated and how high-quality datasets are generated to accelerate system performance. You will operate at the intersection of robotics, machine learning, and human-in-the-loop systems, building the infrastructure and methodologies that connect teleoperation, evaluation, and learning. This includes developing evaluation policies, defining task structures, and contributing to operator-facing interfaces that enable scalable and reliable data collection. The ideal candidate is highly experimental, systems-oriented, and comfortable working across software, robotics, and data pipelines, with a strong focus on turning ambiguous capability goals into measurable and actionable evaluation systems. Key job responsibilities - Design and implement evaluation frameworks to measure robot capabilities across structured tasks, edge cases, and real-world scenarios - Develop task definitions, success criteria, and benchmarking methodologies that enable consistent and reproducible evaluation of policies - Create and refine data collection protocols that generate high-quality, task-relevant datasets aligned with model development needs - Build and iterate on teleoperation workflows and operator interfaces to support efficient, reliable, and scalable data collection - Analyze evaluation results and collected data to identify performance gaps, failure modes, and opportunities for targeted data collection - Collaborate with engineering teams to integrate evaluation tooling, logging systems, and data pipelines into the broader robotics stack - Stay current with advances in robotics, evaluation methodologies, and human-in-the-loop learning to continuously improve internal approaches - Lead technical projects from conception through production deployment - Mentor junior scientists and engineers
Applied Scientist, Prime Video - Content Localization, Understanding & Enrichment
US, WA, Seattle
Prime Video is a first-stop entertainment destination offering customers a vast collection of premium programming in one app available across thousands of devices. Prime members can customize their viewing experience and find their favorite movies, series, documentaries, and live sports – including Amazon MGM Studios-produced series and movies; licensed fan favorites; and programming from Prime Video subscriptions such as Apple TV+, HBO Max, Peacock, Crunchyroll and MGM+. All customers, regardless of whether they have a Prime membership or not, can rent or buy titles via the Prime Video Store, and can enjoy even more content for free with ads. Are you interested in shaping the future of entertainment? Prime Video's technology teams are creating best-in-class digital video experience. As a Prime Video team member, you’ll have end-to-end ownership of the product, user experience, design, and technology required to deliver state-of-the-art experiences for our customers. You’ll get to work on projects that are fast-paced, challenging, and varied. You’ll also be able to experiment with new possibilities, take risks, and collaborate with remarkable people. We’ll look for you to bring your diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. With global opportunities for talented technologists, you can decide where a career Prime Video Tech takes you! As an Applied Scientist, you will apply state of the art natural language processing and computer vision research to video centric digital media. We are looking for scientists with expertise in vision-language models/multimodal LLMs and long-form content understanding (full movies/episode vs. short clips). You will be dealing with architectures that handle long-context understanding and causal reasoning across extended temporal sequences. Key job responsibilities Our team builds multi-modal machine learning technologies to enrich and understand video content. We aim not only to understand individual components within the content itself, but also their relationships to each other to provide a holistic and broader contextual understanding. This powers the next generation of video understanding and search capabilities for Prime Video. About the team Prime Video's Content Localization, Understanding & Enrichment organization is responsible for 1) enabling Prime Video to "see" and "understand" video content including characters, scenes, dialogue, events & visual elements and 2) delivering localized, accessible content that meets a consistent cinematic quality standard at scale. This team's mission is to deeply understand all content and empower all customers with relevant language options, innovative accessibility assists, and rich title-information across all their content-experiences on Prime Video. We create and publish content on-time that's meaningful, accurate, and accessible to every customer globally. We delight our customers by pushing the boundaries of content understanding and enrichment. Through inclusion and innovation, we do the most fulfilling work of our career.
Postdoctoral Researcher – Visual Localization & Navigation | Amazon Last Mile
US, CA, Santa Cruz
Amazon is looking for talented Postdoctoral Scientists to join our research team for a full-time research position focused on visual localization and navigation for real-world applications. Our work focuses on developing next-generation assistive technologies and logistics platforms that rely on robust, scalable visual perception systems. We are building solutions that enable devices and agents to understand, localize within, and navigate complex real-world environments—from indoor spaces with dynamic layouts to large-scale outdoor settings. We are looking for Postdoctoral Scientists to work at the intersection of computer vision, SLAM, and scene understanding—supporting innovations that will be deployed to real systems at global scale. The core technical challenges include building metric-semantic maps of complex environments, performing robust visual relocalization under appearance change, maintaining long-term map consistency, and achieving accurate monocular localization using both geometric and learning-based approaches—all under real-time constraints on real hardware. The solution space is deliberately open-ended. We are looking for researchers who want to push the boundaries of visual localization and spatial AI—and see their work running on real platforms within months. Key job responsibilities In this role you will: * Work closely with a senior science advisor, collaborate with other scientists and engineers, and be part of Amazon’s vibrant and diverse global science community. * Publish your innovation in top-tier academic venues and hone your presentation skills. * Be inspired by challenges and opportunities to invent cutting-edge techniques in your area(s) of expertise. A day in the life 0
Sr Data Scientist, SPX AI Lab, SPX Science
US, WA, Seattle
Amazon Seller Assistant is our flagship GenAI-first, multi-agent system that reimagines Seller experience. Our vision is to provide each seller with a proactive, autonomous, agentic assistant that understands their business and helps them navigate the complexities of selling by anticipating their needs, surfacing insights, resolving issues, taking actions on their behalf, and helping them grow. Amazon Seller Assistant helps millions of sellers on Amazon serve billions of customers worldwide. We are seeking a world-class Senior Data Scientist to help define and build the next generation of Amazon Seller Assistant. You will partner with top-tier scientist, engineers and product teams to launch production-grade agentic capabilities at Amazon's scale — owning your problem space end-to-end, from a crisp customer insight to a shipped product that millions of sellers rely on. Key job responsibilities • Own the science vision, strategy, and roadmap for a key Seller Assistant capability area. • Define and ship agentic experiences — sub-agent onboarding, tool onboarding, evaluations— that solve hard seller problems at scale. • Partner with scientists and engineers to translate frontier AI research into production-grade features sellers trust and depend on. • Design rigorous evaluation frameworks — automated and human-in-the-loop — to measure agent quality, accuracy, and business impact. • Deep-dive into seller data, identify unmet needs, and write compelling PRFAQs that set the direction for your team. • Drive cross-functional alignment across science, engineering, UX, and business teams to deliver with speed and quality. About the team Amazon Seller Assistant team operates at the very frontier of agentic AI and agentic commerce — not as a research group, but as a team shipping production-grade, multi-agent systems used by millions of sellers worldwide. We move with the urgency of a startup and the resources of the world's most customer-obsessed company, the latest breakthroughs in science and engineering into capabilities that sellers rely on every day.
Applied Science Manager, Center for Quantum Computing
US, CA, San Francisco
The Amazon Center for Quantum Computing (CQC) is seeking to hire an Applied Science Manager to lead a team of scientists in the physical design and simulation of superconducting quantum processors. In this role, you will use advanced modeling, simulation, and experimental design to drive improvements in scaling and performance. You will partner with other physics and engineering teams to advance the development of fault-tolerant quantum computers. Key job responsibilities - Hire Applied Scientists from diverse technical backgrounds to design quantum processors and improve the design process - Develop scientific talent through goal setting, feedback, collaborative work, and coaching - Collaborate with other science teams in designing experiments to overcome scaling and performance limitations - Influence engineering team development priorities in enabling systematic processor design and simulation workflows - Manage tactical and strategic initiatives with scientific projects pursued within team - Enable creative and innovative experimentation while striving for operational excellence About the team The Amazon Center for Quantum Computing (CQC) is a multi-disciplinary team of scientists, engineers, and technicians, on a mission to develop a fault-tolerant quantum computer. Inclusive Team Culture Here at Amazon, 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 conferences, inspire us to never stop embracing our uniqueness. 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. 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. Export Control Requirement Due to applicable export control laws and regulations, candidates must be either a U.S. citizen or national, U.S. permanent resident (i.e., current Green Card holder), or lawfully admitted into the U.S. as a refugee or granted asylum, or be able to obtain a US export license. If you are unsure if you meet these requirements, please apply and Amazon will review your application for eligibility.
Data Scientist II - AMZ27351.1
US, CA, San Francisco
Employer: Amazon Web Services, Inc. Position: Data Scientist II - AMZ27351.1 Location: San Francisco, CA Multiple Positions Available: Design and implement scalable and reliable approaches to support or automate decision making throughout the business. Apply a range of data science techniques and tools combined with subject matter expertise to solve difficult business problems and cases in which the solution approach is unclear. Acquire data by building the necessary SQL / ETL queries. Import processes through various company specific interfaces for accessing Oracle, RedShift, and Spark storage systems. Build relationships with stakeholders and counterparts. Analyze data for trends and input validity by inspecting univariate distributions, exploring bivariate relationships, constructing appropriate transformations, and tracking down the source and meaning of anomalies. Build models using statistical modeling, mathematical modeling, econometric modeling, network modeling, social network modeling, natural language processing, machine learning algorithms, genetic algorithms, and neural networks. Validate models against alternative approaches, expected and observed outcome, and other business defined key performance indicators. Implement models that comply with evaluations of the computational demands, accuracy, and reliability of the relevant ETL processes at various stages of production. (40 hours / week, 8:00am-5:00pm, Salary Range $175425 - $212800) Amazon.com is an Equal Opportunity – Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation
Member of Technical Staff, FAR (Frontier AI & Robotics)
US, CA, San Francisco
Join the next revolution in robotics at Amazon's Frontier AI & Robotics team, where you'll work alongside world-renowned AI pioneers to lead key initiatives in robotic intelligence. As a Member of Technical Staff, you'll spearhead the development of breakthrough foundation models that enable robots to perceive, understand, and interact with the world in unprecedented ways. You'll drive technical excellence in areas such as perception, manipulation, science understanding, sim2real transfer, multi-modal foundation models, and multi-task learning, designing novel algorithms that bridge the gap between state-of-the-art research and real-world deployment at Amazon scale. In this role, you'll combine hands-on technical work with scientific leadership, ensuring your team delivers robust solutions for dynamic real-world environments. You'll leverage Amazon's vast computational resources to tackle ambitious problems in areas like very large multi-modal robotic foundation models and efficient, promptable model architectures that can scale across diverse robotic applications. Key job responsibilities - Lead technical initiatives in robotics foundation models, driving breakthrough approaches through hands-on research and development in areas like open-vocabulary panoptic scene understanding, scaling up multi-modal LLMs, sim2real/real2sim techniques, end-to-end vision-language-action models, efficient model inference, video tokenization - Design and implement novel deep learning architectures that push the boundaries of what robots can understand and accomplish - Guide technical direction for specific research initiatives, ensuring robust performance in production environments - Mentor and support fellow scientists while maintaining strong individual technical contributions - Collaborate with engineering teams to optimize and scale models for real-world applications - Influence technical decisions and implementation strategies within your area of focus A day in the life - Develop and implement novel foundation model architectures, working hands-on with our extensive compute infrastructure - Guide and support fellow scientists in solving complex technical challenges, from sim2real transfer to efficient multi-task learning - Lead focused technical initiatives from conception through deployment, ensuring successful integration with production systems - Drive technical discussions within your team and with key stakeholders - Conduct experiments and prototype new ideas using our massive compute cluster - Mentor team members while maintaining significant hands-on contribution to technical solutions 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 At Frontier AI & Robotics, we're not just advancing robotics – we're reimagining it from the ground up. Our team is building the future of intelligent robotics through ground breaking foundation models and end-to-end learned systems. We tackle some of the most challenging problems in AI and robotics, from developing sophisticated perception systems to creating adaptive manipulation strategies that work in complex, real-world scenarios. What sets us apart is our unique combination of ambitious research vision and practical impact. We leverage Amazon's massive computational infrastructure and rich real-world datasets to train and deploy state-of-the-art foundation models. Our work spans the full spectrum of robotics intelligence – from multimodal perception using images, videos, and sensor data, to sophisticated manipulation strategies that can handle diverse real-world scenarios. We're building systems that don't just work in the lab, but scale to meet the demands of Amazon's global operations. Join us if you're excited about pushing the boundaries of what's possible in robotics, working with world-class researchers, and seeing your innovations deployed at unprecedented scale.
See more jobs
View from space of a connected network around planet Earth representing the Internet of Things.
Get more from Amazon Science
Subscribe to our newsletter

Amazon.com | Conditions of Use | Privacy | © 1996-2026 Amazon.com, Inc. or its affiliates

Social