Amazon AI Research Awards

Amazon AI Research Awards is sponsored by Amazon. Amazon seeks to fund AI research on topics such as trustworthy and reliable agentic AI for security operations, threat/intrusion/anomaly detection in agentic AI systems and cloud environments, and building/optimizing foundation models for security tasks.

Call For Proposals | Amazon Research Awards - Amazon Science Information and knowledge management Operations research and optimization Security, privacy, and abuse prevention Our scientific contributions Research from our scientists and collaborators. Our experts present and discuss cutting-edge research at scientific meetings globally.

Information and knowledge management Operations research and optimization Security, privacy, and abuse prevention Our scientific contributions Research from our scientists and collaborators. Our experts present and discuss cutting-edge research at scientific meetings globally. The latest from Amazon researchers Technical deep-dives and perspectives from our scientists.

Research milestones and recent achievements. The latest from Amazon researchers Technical deep-dives and perspectives from our scientists. Research milestones and recent achievements.

Carnegie Mellon University Tennessee State University University of California, Los Angeles University of Illinois Urbana-Champaign University of Southern California University of Texas at Austin Carnegie Mellon University Tennessee State University University of California, Los Angeles University of Illinois Urbana-Champaign University of Southern California University of Texas at Austin Meet the team building useful AI agents.

Try Amazon’s frontier foundation models. Meet the team building useful AI agents. Try Amazon’s frontier foundation models.

Faculty research opportunities on industry-scale technical challenges. Postdoctoral Science Program Early-career research opportunities alongside experienced industry scientists. Faculty research opportunities on industry-scale technical challenges.

Postdoctoral Science Program Early-career research opportunities alongside experienced industry scientists. Awards are structured as unrestricted gifts to the principal investigator's academic institution or organization and as such, Amazon retains no intellectual property rights to the resulting work. Recipients are encouraged to publish outcomes and commit related code to open-source repositories.

Recipients are assigned an Amazon research contact who offers consultation and advice along with opportunities to participate in Amazon events and training sessions. The submission period closed on May 13.

AI for Information Security Advancing possible solutions for some of the most challenging problems in information security Advancing the frontiers of AI agents Advancing science through scale-driven innovation in the next three to five years Setting the standard for security at Amazon Build on Trainium: Accelerating Post-Training Building the future of AI with AWS Trainium Build on Trainium: Kernels for ML Acceleration Building the future of AI with AWS Trainium Pursuing the future of robotics research The submission period closed on November 12.

AI for Information Security Advancing possible solutions for some of the most challenging problems in information security. Advancing the frontiers of AI agents Systems assurance by mathematical proof.

Build on Trainium: Responsible AI Building the future of AI with AWS Trainium Setting the standard for cryptography at Amazon Cybersecurity and Anti-Abuse Technologies Advancing solutions to protect the Web from sophisticated cybercrime, abuse and fraud at scale Data-driven solutions for Devices Sustainability: Optimizing manufacturing and use phase impact Advancing the frontiers of science through transformative ideas The submission period closed on May 7.

AI for Information Security Advancing possible solutions for some of the most challenging problems in information security. Advancing customer protections in the era of artificial intelligence in digital advertising. Advancing the frontiers of AI.

Building the future of AI with AWS Trainium. Advancing the frontiers of science through transformative ideas. The submission period closed on November 13.

AI for Information Security Advancing possible solutions for some of the most challenging problems in information security. Systems assurance by mathematical proof. Advancing the frontiers of machine learning.

Setting the standard for cryptography at Amazon. Welcoming proposals related to data validation, life cycle assessment, biodiversity and more. The submission period closed on May 7.

AI for Information Security Advancing possible solutions for some of the most challenging problems in information security. Addressing the challenges associated with generating consistent, transparent, and accurate carbon measurements. The submission period closed on March 6.

Foundation Model Development Advancing the frontiers of foundation model development. The submission period closed on November 13. Decision letters were sent out March 2024.

AI for Information Security Helping customers achieve the highest levels of security in the cloud. Systems assurance by mathematical proof Advancing the frontiers of machine learning. AWS Cryptography and Privacy Setting the standard for cryptography and privacy at Amazon.

Innovative approaches to designing, building, or operating large-scale database services or distributed cloud services. Making Amazon the most environmentally and socially responsible place to buy or sell goods and services. The submission period closed April 26.

Decision letters were sent out August 2023. AWS AI solicited funding proposals related to generative AI, which focused on innovation related to supporting annotators with machine learning and artificial intelligence. The submission period closed October 26.

Decision letters were sent March 2023. Advancing the frontiers of machine learning. Systems assurance by mathematical proof Pushing the boundaries of science and technology Welcoming proposals related to climate risk/resilience, life cycle assessment, circular strategies, and more.

The submission period closed July 15. Decisions letters were sent December 2022.

AI for Information Security Advancing cybersecurity with AI Amazon Science Community and Machine Learning University Making Amazon the best place in the world to do customer-obsessed science and engineering Breakthroughs in online advertising AWS AI: Human-in-the-loop machine learning and annotation Sharing learnings and ML capabilities as fully managed services The submission period closed January 21.

Decision letters were sent May 2022. The submission period closed October 8. Decision letters were sent March 2022.

AI for Information Security Advancing cybersecurity with AI Data for Social Sustainability Advancing the use of data science for social good Amazon Device Security and Privacy Enabling trustworthy compute environment from edge to cloud Breakthroughs in security, verification, and anomaly detection Advancing the frontiers of machine learning Security assurance, backed by mathematical proof Prime Video - Automating Quality Analysis & Delivery Solving audio/video challenges with machine learning Pursuing the future of robotics research Amazon Advertising - Summer 2021 Breakthroughs in online advertising Alexa Fairness in AI - Spring 2021 AWS Automated Reasoning - Spring 2021 Security assurance, backed by mathematical proof AI for Information Security - Fall 2020 Advancing cybersecurity with AI Alexa Fairness in AI - Fall 2020 Pursuing the future of robotics research Advancing the frontiers of machine learning AWS Automated Reasoning - Fall 2020 Security assurance, backed by mathematical proof Economists in this role partner with business stakeholders to distill complex problems into testable economic questions and generate actionable insights.

They collaborate with engineers and scientists to estimate models on large-scale data, design pilots, measure impact, and scale successful prototypes into improved policies and programs. They leverage AI tools to scale economic study for broader business impact. They communicate findings to business leaders, incorporate feedback, and deliver customer-centric solutions at scale.

Applied Scientist III, Amazon - Ads Nova Are you passionate about solving big problems from ground-up? Do you enjoy building new state-of-the-art products at internet scale? Come lead the innovation in this startup team, vertical ad products.

This is a green field problem without a known answer or a pattern to follow. We have ambitious vision to simplify full funnel advertising solutions, at scale, with specialized agentic AI-powered models and diversify the demand to strategic verticals including finserv, autos, locals.. etc. We are seeking an experienced Applied Scientist to drive innovation in our Ads Foundational Model.

In this individual contributor role, you will apply advanced machine learning techniques to improve advertiser performance and customer experience. Key job responsibilities As an Applied Scientist on this team, you will: 1. Develop and drive the science strategy for Ads Foundational Model (Ads-FM), aligning it with the program's objectives and overall business goals.

2. Identify high-impact opportunities within Ads-FM program and lead the ideation, planning, and execution of science initiatives to address them. 3.

Build and deploy machine learning models using computer vision, natural language processing, and deep learning to evaluate and enhance ad effectiveness. 4. Develop algorithms that extract meaningful signals from image, video, and audio content to predict and improve customer engagement 5.

Leverage Amazon's extensive data repository to create predictive models that generate actionable recommendations for more compelling ad creative 6. Collaborate with business leaders and cross-functional teams to implement ML-powered solutions 7. Contribute to the ML roadmap for the Ads-FM program through innovation and research.

Principal Economist, Stores Economics and Science This role will contribute to developing the Economics and Science products and services in the Fee domain, with specialization in supply chain systems and fees. Through the lens of economics, you will develop causal links for how Amazon, Sellers and Customers interact. You will be a key and senior scientist, advising Amazon leaders how to price our services.

You will work on developing frameworks and scaleable, repeatable models supporting optimal pricing and policy in the two-sided marketplace that is central to Amazon's business. The pricing for Amazon services is complex. You will partner with science and technology teams across Amazon including Advertising, Supply Chain, Operations, Prime, Consumer Pricing, and Finance.

We are looking for an experienced Principal Economist to improve our understanding of seller Economics, enhance our ability to estimate the causal impact of fees, and work with partner teams to design pricing policy changes. In this role, you will provide guidance to scientists to develop econometric models to influence our fee pricing worldwide.

You will lead the development of causal models to help isolate the impact of fee and policy changes from other business actions, using experiments when possible, or observational data when not.

Key job responsibilities The ideal candidate will have extensive Economics knowledge, demonstrated strength in practical and policy relevant structural econometrics, strong collaboration skills, proven ability to lead highly ambiguous and large projects, and a drive to deliver results.

They will work closely with Economists, Data / Applied Scientists, Strategy Analysts, Data Engineers, and Product leads to integrate economic insights into policy and systems production. Familiarity with systems and services that constitute seller supply chains is a plus but not required. About the team The Stores Economics and Sciences team is a central science team that supports Amazon's Retail and Supply Chain leadership.

We tackle some of Amazon's most challenging economics and machine learning problems, where our mandate is to impact the business on massive scale. Research Scientist, Private Brands Intelligence - SCIT Science The Private Brands team is looking for a Research Scientist to join the team in building science solutions at scale.

Our team applies Optimization, Machine Learning, Statistics, Causal Inference, and Econometrics/Economics to derive actionable insights about the complex economy of Amazon’s retail business and develop Statistical Models and Algorithms to drive strategic business decisions and improve operations. We are an interdisciplinary team of Scientists, Engineers, and Economists.

Key job responsibilities You will work with business leaders, scientists, and economists to translate business and functional requirements into concrete deliverables, including the design, development, testing, and deployment of highly scalable optimization solutions and ML models.

This is a unique, high visibility opportunity for someone who wants to have business impact, dive deep into large-scale problems, enable measurable actions on the consumer economy, and work closely with scientists and economists. As a Research Scientist, you bring business and industry context to science and technology decisions.

You set the standard for scientific excellence and make decisions that affect the way we build and integrate algorithms. Your solutions are exemplary in terms of algorithm design, clarity, model structure, efficiency, and extensibility. You tackle intrinsically hard problems, acquiring expertise as needed.

You decompose complex problems into straightforward solutions. We are particularly interested in candidates with experience in Operations Research and predictive models and working with distributed systems. Academic and/or practical background in Operations Research, Machine Learning and Reinforcement Learning are particularly relevant for this position.

To know more about Amazon science, Please visit https://www. amazon. science Sr Manager, Applied Science, Alexa for Shopping (Rufus) Alexa for Shopping (previously Rufus) is seeking a Senior Manager, Applied Science to lead multidisciplinary teams of Applied Scientists and Machine Learning Engineers building next-generation conversational AI and multi-agent systems powering customer-facing experiences at scale.

This leader will drive both scientific innovation and execution across large language models (LLMs), agent orchestration, retrieval and grounding systems, evaluation frameworks, and scalable AI infrastructure. The role requires a combination of deep technical judgment, organizational leadership, product and engineering partnership, and operational excellence.

The ideal candidate has a strong track record of building high-performing science and engineering teams, translating ambiguous business problems into scalable AI solutions, and delivering measurable customer impact through applied machine learning and generative AI technologies.

Key job responsibilities - Lead and grow teams of Applied Scientists and Machine Learning Engineers working on conversational AI and multi-agent orchestration systems. - Define and drive technical strategy for large-scale generative AI systems, including LLM routing, prompting, grounding, memory, tool use, personalization, and response optimization.

- Partner closely with Product, Engineering, and Tech leadership to align AI investments with long-term business and customer goals. - Drive end-to-end delivery of production AI systems balancing quality, latency, scalability, safety, and operational reliability. - Establish scientific and engineering best practices across experimentation, evaluation, model iteration, and production deployment.

- Lead roadmap prioritization and execution across research innovation and product delivery timelines. - Build scalable evaluation methodologies and quality frameworks for multilingual and global customer experiences. - Mentor and develop technical leaders across both science and engineering disciplines.

- Foster a high-performance culture centered on customer obsession, innovation, operational excellence, and strong cross-functional collaboration. Human-Robot Interaction Applied Scientist , Fauna We are seeking a Human-Robot Interaction (HRI) Applied Scientist to develop cutting-edge interactions that make robots feel alive, personal, and fun.

In this role, you will focus on verbal and non-verbal conversational systems, social dynamics, memory, and long-term relationship formation between robots, their environments, and the people they interact with. Your contributions will be essential in advancing robotics by enabling expressive, socially intelligent, and trustworthy interactions between robots and humans.

Key job responsibilities - Develop interactive systems that leverage large language models, multimodal inputs and outputs, reinforcement learning from human feedback, or other advanced techniques to achieve fluid, engaging, and socially appropriate robot behavior - Design and implement intelligent conversational systems that handle turn-taking, grounding, interruption, and incorporates context drawn from a robot's physical environment and shared history with a user - Integrate perceptual sensor streams including gaze, facial expression, gesture, posture, and more to understand social context and produce coherent, lifelike interactions.

- Develop memory and personalization systems that allow robots to form lasting relationships with individual users, learn their environments, and adapt their behavior over weeks and months - Stay updated on advancements in HRI, NLP, multimodal AI, and cognitive and social science to apply cutting-edge techniques to robot interaction challenges - Lead technical projects from conception through production deployment - Mentor junior scientists and engineers - Bridge research initiatives with practical engineering implementation Applied Scientist II, Buyer Risk Prevention (BRP) Do you want to join an innovative team of scientists applying machine learning and advanced statistical techniques to protect Amazon customers and enable a trusted eCommerce experience?

Are you excited about modeling terabytes of data and building state-of-the-art algorithms to solve complex, real-world fraud and risk challenges? Do you enjoy owning end-to-end machine learning problems, directly influencing customer experience and company profitability, while collaborating in a diverse, high-performing team? If so, the Amazon Buyer Risk Prevention (BRP) Machine Learning team may be the right fit for you.

We are seeking an Applied Scientist to design, develop, and deploy advanced algorithmic systems that safeguard millions of transactions every day. In this role, you will independently drive model development from problem formulation to production deployment, build scalable ML solutions, and leverage emerging technologies—including Generative AI and LLMs—to enhance fraud detection and next-generation risk prevention systems.

Key job responsibilities Own end-to-end development of machine learning models for large-scale risk management systems Analyze large volumes of historical and real-time data to identify fraud patterns and emerging risk trends Design, develop, validate, and deploy innovative models to production environments Apply GenAI/LLM technologies to automate risk evaluation and improve operational efficiency Collaborate closely with software engineering teams to implement scalable, real-time model solutions Partner with operations and business stakeholders to translate risk insights into measurable impact Establish scalable and automated processes for data analysis, model experimentation, validation, and monitoring Track model performance and business metrics; communicate insights clearly to technical and non-technical stakeholders Research and implement novel machine learning and statistical methodologies Applied Scientist, Buyer Risk Prevention (BRP) Do you want to join an innovative team applying machine learning and advanced statistical techniques to protect Amazon customers and enable a trusted eCommerce experience?

Are you excited about working with large-scale datasets and developing models that solve real-world fraud and risk challenges? If so, the Amazon Buyer Risk Prevention (BRP) Machine Learning team may be the right fit for you. We are seeking an Applied Scientist to help develop scalable machine learning solutions that safeguard millions of transactions every day.

In this role, you will partner with senior scientists and engineers to translate business problems into data-driven solutions, build and evaluate models, and contribute to next-generation risk prevention systems, including applications of Generative AI and LLM technologies.

Key job responsibilities Apply machine learning and statistical techniques to build and improve risk management models Analyze large-scale historical data to identify risk patterns and emerging trends Develop, validate, and deploy innovative models under the guidance of senior scientists Experiment with emerging technologies, including GenAI/LLMs, to enhance automation and risk evaluation Collaborate closely with software engineers to implement models in real-time production systems Partner with operations and business teams to improve risk policies and operational efficiency Build scalable, automated pipelines for data analysis, model training, and validation Monitor model performance and provide clear reporting on key risk and business metrics Research and prototype new modeling approaches to improve system performance Applied Scientist II, Buyer Risk Prevention (BRP) Do you want to join an innovative team of scientists applying machine learning and advanced statistical techniques to protect Amazon customers and enable a trusted eCommerce experience?

Are you excited about modeling terabytes of data and building state-of-the-art algorithms to solve complex, real-world fraud and risk challenges? Do you enjoy owning end-to-end machine learning problems, directly influencing customer experience and company profitability, while collaborating in a diverse, high-performing team? If so, the Amazon Buyer Risk Prevention (BRP) Machine Learning team may be the right fit for you.

We are seeking an Applied Scientist to design, develop, and deploy advanced algorithmic systems that safeguard millions of transactions every day. In this role, you will independently drive model development from problem formulation to production deployment, build scalable ML solutions, and leverage emerging technologies—including Generative AI and LLMs—to enhance fraud detection and next-generation risk prevention systems.

Key job responsibilities Own end-to-end development of machine learning models for large-scale risk management systems Analyze large volumes of historical and real-time data to identify fraud patterns and emerging risk trends Design, develop, validate, and deploy innovative models to production environments Apply GenAI/LLM technologies to automate risk evaluation and improve operational efficiency Collaborate closely with software engineering teams to implement scalable, real-time model solutions Partner with operations and business stakeholders to translate risk insights into measurable impact Establish scalable and automated processes for data analysis, model experimentation, validation, and monitoring Track model performance and business metrics; communicate insights clearly to technical and non-technical stakeholders Research and implement novel machine learning and statistical methodologies Senior Applied Scientist , Buyer Risk Prevention (BRP) Do you want to lead the development of advanced machine learning systems that protect millions of customers and power a trusted global eCommerce experience?

Are you passionate about modeling terabytes of data, solving highly ambiguous fraud and risk challenges, and driving step-change improvements through scientific innovation? If so, the Amazon Buyer Risk Prevention (BRP) Machine Learning team may be the right place for you.

We are seeking a Senior Applied Scientist to define and drive the scientific direction of large-scale risk management systems that safeguard millions of transactions every day. In this role, you will lead the design and deployment of advanced machine learning solutions, influence cross-team technical strategy, and leverage emerging technologies—including Generative AI and LLMs—to build next-generation risk prevention platforms.

Key job responsibilities Lead the end-to-end scientific strategy for large-scale fraud and risk modeling initiatives Define problem statements, success metrics, and long-term modeling roadmaps in partnership with business and engineering leaders Design, develop, and deploy highly scalable machine learning systems in real-time production environments Drive innovation using advanced ML, deep learning, and GenAI/LLM technologies to automate and transform risk evaluation Influence system architecture and partner with engineering teams to ensure robust, scalable implementations Establish best practices for experimentation, model validation, monitoring, and lifecycle management Mentor and raise the technical bar for junior scientists through reviews, technical guidance, and thought leadership Communicate complex scientific insights clearly to senior leadership and cross-functional stakeholders Identify emerging scientific trends and translate them into impactful production solutions Get more from Amazon Science