AWS Cryptography call for proposals - Fall 2025

AWS Cryptography call for proposals - Fall 2025 is sponsored by Amazon Research Awards. Amazon seeks proposals in cryptography research, specifically in areas like formal proof of security properties for cryptographic primitives, practical optimizations for cryptographic transport protocols, cryptanalysis, novel cryptographic constructions, and formal/static verifi…

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 Try Amazon’s frontier foundation models.

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 Applied Scientist, Observability, Prime Video Data Scientist III - AMZ9976173 MULTIPLE POSITIONS AVAILABLE Employer: AMAZON.

COM SERVICES LLC Offered Position: Data Scientist III Job Location: Santa Clara, California Job Number: AMZ9976173 Position Responsibilities: Own the data science elements of various products to help with data-based decision making, product performance optimization, and product performance tracking. Work directly with product managers to help drive the design of the product.

Work with Technical Product Managers to help drive the build planning. Translate business problems and products into data requirements and metrics. Initiate the design, development, and implementation of scientific analysis projects or deliverables.

Own the analysis, modelling, system design, and development of data science solutions for products. Write documents and make presentations that explain model/analysis results to the business. Bridge the degree of uncertainty in both problem definition and data scientific solution approaches.

Build consensus on data, metrics, and analysis to drive business and system strategy. 40 hours / week, 8:00am-5:00pm, Salary Range: $183,000/year to $247,600/year. Amazon is a total compensation company.

Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, visit: https://www. aboutamazon.

com/workplace/employee-benefits. Amazon. com is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation.

#0000 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, Secure 3P Tools Amazon Security is seeking an Applied Scientist to work on GenAI acceleration within the Secure Third Party Tools (S3T) organization.

The S3T team has bold ambitions to re-imagine security products that serve Amazon's pace of innovation at our global scale. This role will focus on leveraging large language models and agentic AI to transform third-party security risk management, automate complex vendor assessments, streamline controllership processes, and dramatically reduce assessment cycle times.

You will drive builder efficiency and deliver bar-raising security engagements across Amazon. Key job responsibilities Own and drive end-to-end technical delivery for scoped science initiatives focused on third-party security risk management, independently defining research agendas, success metrics, and multi-quarter roadmaps with minimal oversight.

Understanding approaches to automate third-party security review processes using state-of-the-art large language models, development intelligent systems for vendor assessment document analysis, security questionnaire automation, risk signal extraction, and compliance decision support.

Build advanced GenAI and agentic frameworks including multi-agent orchestration, RAG pipelines, and autonomous workflows purpose-built for third-party risk evaluation, security documentation processing, and scalable vendor assessment at enterprise scale. Build ML-powered risk intelligence capabilities that enhance third-party threat detection, vulnerability classification, and continuous monitoring throughout the vendor lifecycle.

Coordinate with Software Engineering and Data Engineering to deploy production-grade ML solutions that integrate seamlessly with existing third-party risk management workflows and scale across the organization. About the team Security is central to maintaining customer trust and delivering delightful customer experiences. At Amazon, our Security organization is designed to drive bar-raising security engagements.

Our vision is that Builders raise the Amazon security bar when they use our recommended tools and processes, with no overhead to their business. Diverse Experiences Amazon Security 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 Amazon Security? At Amazon, security is central to maintaining customer trust and delivering delightful customer experiences.

Our organization is responsible for creating and maintaining a high bar for security across all of Amazon’s products and services. We offer talented security professionals the chance to accelerate their careers with opportunities to build experience in a wide variety of areas including cloud, devices, retail, entertainment, healthcare, operations, and physical stores.

Inclusive Team Culture In Amazon Security, it’s in our nature to learn and be curious. Ongoing DEI events and learning experiences inspire us to continue learning and to embrace our uniqueness. Addressing the toughest security challenges requires that we seek out and celebrate a diversity of ideas, perspectives, and voices.

Training & 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, training, and other career-advancing resources here to help you develop into a better-rounded professional. Work/Life Balance We value work-life harmony.

Achieving success at work should never come at the expense of sacrifices at home, which is why flexible work hours and arrangements are part of our culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve.

Economist, Amazon Customer Service Amazon Customer Service (CS) Data Intelligence builds the data and Artificial Intelligence (AI) foundations for CS to ensure Amazon delivers the best customer service possible. CS Economics sits within CS DI and contributes to the CS knowledge base and decision frameworks. CS Economics seeks economists to apply economic methods to solve business problems.

The ideal candidate will work with engineers and applied scientists to design models that leverage large scale and unstructured data, design scalable agents for non-tech CS partners to understand the impact of their actions, and propose mechanism designs to robustly match customers to our services.

CS Economics is looking for optimistic critical-thinkers who combine a strong technical economic toolbox with a desire to learn from other disciplines, and who know how to execute and deliver on big ideas as part of an interdisciplinary technical team. Ideal candidates enjoy working in a team setting with individuals from diverse disciplines and backgrounds.

They will work with teammates to develop scientific models and conduct data analysis, modeling, and experimentation that is necessary for estimating and validating models. They will work closely with engineering teams to develop scalable data resources to support rapid insights, and take successful models and findings into production as new products and services.

They will be customer-centric and will communicate scientific approaches and findings to business leaders, listening to and incorporate their feedback, and delivering successful scientific solutions.

Key job responsibilities - Design and conduct rigorous evaluations of CS actions - Develop experiments to evaluate product launches - Communicate complex findings to business stakeholders in clear, actionable terms - Work with engineering teams to develop scalable tools that automate and streamline evaluation processes A day in the life Work with teammates to apply economic methods to business problems, e.g., identify the appropriate research question and identification strategy, write code to estimate heterogeneous treatment effects or conduct experiment analysis, write and present a document with findings to business leaders.

We collaborate with partner teams within and outside of CS throughout the process, from understanding their challenges, to developing a research agenda that will address those challenges, to help them implement solutions. About the team Amazon Customer Service (CS) Economics provides estimates and measures of the causal impact of CS actions on costs and benefits.

We build agents and guide leadership to establish processes to scale valid experimentation, causal inference, and mechanism design. Senior Applied Scientist, PXT Do you want to leverage your expertise in translating innovative science into impactful products to improve the lives and work of over a million people worldwide? If so, People eXperience Technology Core Science team would love to discuss how you can make that a reality.

Our team is an interdisciplinary team that uses behavioral science, statistics, and machine learning to identify products, mechanisms, and process improvements that enhance Amazonians' well-being and their ability to deliver value for Amazon's customers. We collaborate with HR teams across Amazon to make Amazon PXT the most scientific human resources organization in the world.

In this role, you will spearhead science design and technical implementation innovations across our talent solution science work-streams. You'll enhance existing models and create new ones, empowering leaders throughout Amazon to make data-driven business decisions. You'll collaborate with scientists and engineers to deliver solutions while working closely with business stakeholders to address their specific needs.

Your work will span various business domains (corporate, operations, safety) and analysis levels (individual, group, organizational), utilizing a range of modeling approaches (linear, tree-based, deep neural networks, and LLM-based). You'll develop end-to-end ML solutions from problem formulation to deployment, maintaining high scientific standards and technical excellence throughout the process.

As an Applied Scientist, you'll also contribute to the team's science strategy, keeping pace with emerging AI/ML trends. You'll mentor junior scientists, fostering their growth by identifying high-impact opportunities. Your guidance will span different analysis levels and modeling approaches, enabling stakeholders to make informed, strategic decisions.

If you excel at building advanced scientific solutions and are passionate about developing technologies that drive organizational change in the AI era, join us as we work hard, have fun, and make history.

Key job responsibilities Key job responsibilities • Model Development & Innovation: Design and implement novel GenAI/LLM solutions using foundation models (e.g., Claude, GPT) and AWS services including Amazon Bedrock, SageMaker, and other AWS AI/ML tools • Research & Experimentation: Conduct applied research to advance the state-of-the-art in LLM applications, including prompt engineering, few-shot learning, fine-tuning, and model evaluation • Production Deployment: Build scalable, production-ready AI systems that serve millions of requests with high reliability, low latency, and cost efficiency • Cross-Functional Collaboration: Partner with product managers, engineers, and business stakeholders to translate business requirements into technical solutions and drive measurable impact • Technical Leadership: Mentor junior scientists, contribute to technical strategy, and establish best practices for GenAI development across the organization • Evaluation & Metrics: Design rigorous evaluation frameworks to measure model performance, bias, safety, and business impact • Documentation & Influence: Publish technical papers, create documentation, and influence both technical and non-technical audiences About the team The People eXperience and Technology (PXT) Core Science Team uses science, engineering, and customer-obsessed problem solving to proactively identify mechanisms, process improvements, and products that simultaneously improve Amazon and Amazonians' lives, wellbeing, and value of work.

As an interdisciplinary team combining talents from machine learning, statistics, economics, behavioral science, engineering, and product development, the Core Science team develops and delivers measurable solutions through innovation and rapid prototyping to accelerate informed, accurate, and reliable decision-making backed by science and data.

We are building a talent intelligence layer — fusing natural language understanding, network science, and large-scale predictive modeling into a unified platform that continuously learns from how people work, collaborate, and grow across one of the world's largest and most complex workforces.

Senior Applied Scientist, ASCS AI Lab Team We are seeking a Senior Applied Scientist to join our team in developing pioneering AI research, Generative AI, Agentic AI, Large Language Models (LLMs), Diffusion and Flow Models, and other advanced Machine Learning and Deep Learning solutions for Amazon Selection and Catalog Systems, within the AI Lab Team.

This role offers a unique opportunity to work on AI research and AI products that will shape the future of online shopping experiences. Our team operates at the forefront of AI research and development, working on challenges that directly impact millions of customers worldwide. We push the boundaries of AI at both the foundational and application layers.

As a Senior Applied Scientist, you will have the chance to experiment with LLMs and deep learning techniques, apply your research to solve real-world problems at an unprecedented scale, and collaborate with experienced scientists to contribute to Amazon's scientific innovation. Join us in redefining the future of shopping. Your work will directly influence how customers interact with the world's largest online store.

Key job responsibilities - Design and implement novel AI solutions for Amazon catalog of products - Develop and train state-of-the-art LLMs, Diffusion Models, and other Generative AI models - Build and deploy autonomous AI Agents in Amazon production ecosystem - Scale AI models to handle billions of diverse products across multiple languages and geographies - Conduct research in areas such as Autonomous AI Agents, Generative AI, Language Modeling, Multi-modality Computer Vision, Diffusion Models, Reinforcement Learning - Collaborate with cross-functional teams to integrate AI models into Amazon's production ecosystem - Contribute to the scientific community through publications and conference presentations Sr. Applied Scientist, Pricing Science Here's the job description with causal ML woven in: We are looking for a talented, organized, and customer-focused applied researcher to join our Pricing Optimization science group, with a charter to measure, refine, and launch customer-obsessed improvements to our algorithmic pricing and promotion models across all products listed on Amazon.

This role requires an individual with exceptional machine learning modeling and architecture expertise — particularly in deep learning, neural networks, and transformer-based architectures applied to price prediction and forecasting problems.

Equally important is deep expertise in causal machine learning — including causal inference, treatment-effect estimation, and experimentation methods (e.g., uplift modeling, double/debiased machine learning, instrumental variables, and A/B and quasi-experimental design) — to isolate the true impact of pricing and promotion decisions on customer behavior and business outcomes.

The ideal candidate brings a strong foundation in applied statistics and probabilistic modeling, excellent cross-functional collaboration skills, 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.

Key job responsibilities See the big picture. Understand and influence the long-term vision for Amazon's science-based competitive, perception-preserving pricing techniques. Develop and advance price prediction models leveraging deep learning frameworks, transformer architectures, and advanced statistical methods to drive pricing accuracy at scale.

Build strong collaborations. Partner with product, engineering, and science teams within Pricing & Promotions to deploy machine learning price estimation and error correction solutions at Amazon scale. Design and implement neural network-based architectures — including sequence models and transformers — for large-scale price prediction and optimization.

Stay informed. Establish mechanisms to stay up to date on the latest scientific advancements in deep learning, transformer architectures, applied statistics, neural network design, probabilistic forecasting, and multi-objective optimization techniques. Identify opportunities to apply them to relevant Pricing & Promotions business problems.

Keep innovating for our customers. Foster an environment that promotes rapid experimentation, continuous learning, and incremental value delivery. Leverage statistical rigor and modern deep learning approaches to validate hypotheses and drive measurable pricing improvements.

Successfully execute & deliver. Apply your exceptional technical machine learning expertise — including deep neural networks, attention-based models, and applied statistical analysis — to incrementally move the needle on some of our hardest pricing problems. A day in the life We are hiring a Sr. Applied Scientist to drive our pricing optimization initiatives.

We drive cross-domain and cross-system improvements through: * shape and extend our RL optimization platform - a pricing centric tool that automates the optimization of various system parameters and price inputs. * Error detection and price quality guardrails at scale.

* Identifying opportunities to optimally price across systems and contexts (marketplaces, request types, event periods) Price is a highly relevant input into Stores architectures; this role creates the opportunity to drive extremely large impact (measured in Bs not Ms), but demands careful thought and clear communication.

About the team The Pricing Optimization science group builds and refines Amazon's algorithmic pricing and promotion models at scale. Our team combines expertise in deep learning, transformer architectures, applied statistics, and probabilistic forecasting to develop price prediction systems that directly impact the customer experience.

The team also brings hands-on experience with causal modeling and inference — including uplift modeling and treatment effect estimation — to rigorously measure the impact of pricing decisions on customer behavior and business outcomes. We partner closely with product, engineering, and business teams to take solutions from research through production deployment.

Applied Scientist II, ASCS AI Lab Team We are seeking an Applied Scientist II to join our team in developing pioneering AI research, Generative AI, Agentic AI, Large Language Models (LLMs), Diffusion and Flow Models, and other advanced Machine Learning and Deep Learning solutions for Amazon Selection and Catalog Systems, within the AI Lab Team.

This role offers a unique opportunity to work on AI research and AI products that will shape the future of online shopping experiences. Our team operates at the forefront of AI research and development, working on challenges that directly impact millions of customers worldwide. We push the boundaries of AI at both the foundational and application layers.

As a Applied Scientist, you will have the chance to experiment with LLMs and deep learning techniques, apply your research to solve real-world problems at an unprecedented scale, and collaborate with experienced scientists to contribute to Amazon's scientific innovation. Join us in redefining the future of shopping. Your work will directly influence how customers interact with the world's largest online store.

Key job responsibilities - Design and implement novel AI solutions for Amazon catalog of products - Develop and train state-of-the-art LLMs, Diffusion Models, and other Generative AI models - Build and deploy autonomous AI Agents in Amazon production ecosystem - Scale AI models to handle billions of diverse products across multiple languages and geographies - Conduct research in areas such as Autonomous AI Agents, Generative AI, Language Modeling, Multi-modality Computer Vision, Diffusion Models, Reinforcement Learning - Collaborate with cross-functional teams to integrate AI models into Amazon's production ecosystem - Contribute to the scientific community through publications and conference presentations Sr. Applied Scientist, MAPLE Are you excited by the idea of developing personalized experiences for Amazon customers as they shop?

Are you looking for new challenges and to solve hard science problems while applying state-of-the-art recommendation system modeling and GenAI techniques? Join us and you'll help millions of customers make informed purchase decisions while also advancing the state of Amazon's science by publishing research!

Key job responsibilities - Participate in the design, development, evaluation, deployment and updating of data-driven models for shopping personalization.

- Develop and test new signals for improving recommendation models - Use supervised and uplift learning algorithms to improve customer experience - Contribute to production code and science tooling - Design A/B tests and conduct statistical analysis on their results - Work with distributed machine learning and statistical algorithms to harness enormous volumes of data at scale to serve our customers - Work closely with internal stakeholders like the business teams, engineering teams and partner teams and align them with respect to your focus area - Present and publish science research internally and externally, contributing to Amazon's science community - Mentor junior engineers and scientists.

About the team Our team's mission is to surface the right payments-related recommendations to customers at the right time, helping create a rewarding and successful shopping experience for Amazon's customers. Our team's culture is highly collaborative, with an emphasis on supporting each other and learning from one another. We dedicate time each week to focus on personal development and expanding our knowledge as a team.

We also highly value having a big impact, both for Amazon's business and for our customers. Get more from Amazon Science