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Automated Reasoning Call for Proposals — Fall 2023

Systems assurance by mathematical proof

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About this CFP

Amazon is committed to helping customers achieve the highest levels of security, availability, and robustness in the cloud. Automated reasoning is the application of mathematical logic to answer questions and prove properties about critical computer systems. Using automated reasoning, Amazon is able to detect previously missed misconfigurations, or show their absence, and to prove that protocols and programs work as intended. Our work in provable security and privacy aims to provides higher assurance for customers. We anticipate funding research in these categories:

  • Automated reasoning
  • Abstract interpretation
  • Correct-by-construction software
  • Dafny
  • Efficient reasoning with quantifiers
  • Improvements to SAT, SMT, and CHC solvers
  • Model checking
  • Novel applications of automated reasoning
  • Provable Privacy
  • Software synthesis and optimization
  • Software verification, e.g., C, Rust, Java
  • Static analysis
  • Theorem proving
  • The use of automated reasoning to improve generative AI techniques
  • Using automated reasoning in combination with generative AI techniques
  • Verification of distributed protocols and systems
  • Verification of randomized algorithms

Timeline

Submission period: September 21, 2023 - November 13, 2023 (11:59PM Pacific Time)

Decision letters will be sent out March 2024

Award details

Selected Principal Investigators (PIs) may receive the following:

  • Unrestricted funds, no more than $80,000 USD on average
  • AWS Promotional Credits, no more than $20,000 USD on average
  • Training resources, including AWS tutorials and hands-on sessions with Amazon scientists and engineers

Amazon Research Awards (ARA) are structured as one-year unrestricted gifts. The budget should include a list of expected costs specified in USD, and should not include administrative overhead costs. The final award amount will be determined by the awards panel.

Eligibility requirements

Please refer to the ARA Program rules on the Rules and Eligibility page.

Proposal requirements

Proposals should be prepared according to the proposal template. Proposals should answer the following questions:

  1. Does your work target analysis of protocols, code, or configuration? Please provide information about your domain and the type of analysis.
  2. What are the current applications of your work? (e.g., libraries, codebases, or industry code).
  3. What are potential applications of your work to Amazon?
  4. What assumptions are made by your work? If the techniques proposed are sound: What are issues that may invalidate this result?
  5. If your work involves the development and maintenance of a tool:
    1. Under what license is or will your tool be released?
    2. What on-boarding/tutorial material is available?
    3. Is your tool actively maintained (i.e., commits within last 3 months)? How many active contributors does your project have?

Selection criteria

ARA funding decisions will be based on potential impact to automated reasoning research, and the development of the automated reasoning scientific community. Amazon's commitment to developing the automated reasoning scientific community includes increasing the number of university researchers engaged in automated reasoning research. We are also committed to increasing the diversity of the automated reasoning community at all levels.

Expectations from recipients

To the extent deemed reasonable, Award recipients should acknowledge the support from ARA. Award recipients will inform ARA of publications, presentations, code and data releases, blogs/social media posts, and other speaking engagements referencing the results of the supported research or the Award. Award recipients are expected to provide updates and feedback to ARA via surveys or reports on the status of their research. Award recipients will have an opportunity to work with ARA on an informational statement about the awarded project that may be used to generate visibility for their institutions and ARA.

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Applied Scientist, AWS Automated Reasoning
US, MA, Boston
Applied Scientists in AWS Automated Reasoning are dedicated to making AWS the best computing service in the world for customers who require advanced and rigorous solutions for automated reasoning, privacy, and sovereignty. Key job responsibilities The successful candidate will: - Solve large or significantly complex problems that require deep knowledge and understanding of your domain and scientific innovation. - Own strategic problem solving, and take the lead on the design, implementation, and delivery for solutions that have a long-term quantifiable impact. - Provide cross-organizational technical influence, increasing productivity and effectiveness by sharing your deep knowledge and experience. - Develop strategic plans to identify fundamentally new solutions for business problems. - Assist in the career development of others, actively mentoring individuals and the community on advanced technical issues. A day in the life This is a unique and rare opportunity to get in early on a fast-growing segment of AWS and help shape the technology, product and the business. You will have a chance to utilize your deep technical experience within a fast moving, start-up environment and make a large business and customer impact. About the team Diverse Experiences Amazon Automated Reasoning 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 Automated Reasoning? At Amazon, automated reasoning is central to maintaining customer trust and delivering delightful customer experiences. Our organization is responsible for creating and maintaining a high bar for automated reasoning across all of Amazon's products and services. We offer talented automated reasoning 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 Automated Reasoning, 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 automated reasoning 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.
Senior Applied Scientist, Buyer Risk Prevention (BRP)
IN, KA, Bengaluru
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
Principal Applied Scientist, Amazon Payments
US, WA, Seattle
We are looking for a Principal Applied Scientist for Amazon Payments AI/ML Team, which contributes science and science-related engineering work for Amazon’s Payments Artificial Intelligence services (e.g. Amazon Payments Recommendation, Prediction, GEN AI platform to enable SOP automation and new projects already underway). In this role, you will work with your peers and senior management to set the direction for Amazon’s AI efforts. Our mission is to put the power of AI in the hands of every developer. You will be responsible for mentoring a team of applied scientists. You will be responsible for creating a strong environment for applied scientists, with a focus on recruiting, retaining and developing top talent. You will partner with engineering leaders to deliver remarkable new Amazon Payments services and features that leverage Machine Learning and GenAI. As a Principal Applied Scientist, you will identify research directions, create roadmaps for forward-looking research and communicate them to senior leadership, and work closely with engineering teams to bring research to production. You will work with teams of talented scientists, and fill the ranks by attracting the best scientists in machine learning, e.g. Amazon payments recommendation and natural language processing for SOP automation. You will work with talented peers and leverage Amazon’s heterogeneous data sources and large-scale computing resources.
Applied Scientist, Sponsored Products & Brands
US, WA, Seattle
The Sponsored Products and Brands team at Amazon Ads is re-imagining the advertising landscape through industry leading 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. Calling all inventors to work on exciting new opportunities in Sponsored Products. We are a highly motivated, collaborative and fun-loving group with an entrepreneurial spirit and bias for action. You will join a newly-founded team with a broad mandate to experiment and innovate, with a focus on driving growth of sponsored products ad experiences across Amazon stores worldwide. This broad charter gives us the flexibility to explore and apply scientific techniques to novel product problems. You will have the satisfaction of seeing your work improve the experience of millions of Amazon shoppers worldwide while driving quantifiable revenue impact. More importantly, you will have the opportunity to broaden your technical skills, and be a science leader in an environment that thrives on creativity, experimentation, and product innovation. Key job responsibilities - Tackle and solve challenging science and business problems that balance the interests of advertisers, shoppers, and Amazon. - Drive end-to-end Machine Learning projects that have a high degree of ambiguity, scale, complexity. - Develop real-time machine learning algorithms to allocate billions of ads per day in advertising auctions. - Develop efficient algorithms for multi-objective optimization using deep learning methods to find operating points for the ad marketplace then evolve them - Research new and innovative machine learning approaches. - Recruit Scientists to the team and provide mentorship.
Economics - Intern - EU Stores, Economic Decision Science
GB, London
Are you excited about applying economic models and methods using large data sets to solve real world business problems? Then join the Economic Decision Science (EDS) team. EDS is an economic science team based in the EU Stores business. The teams goal is to optimize and automate business decision making in the EU business and beyond. An internship at Amazon is an opportunity to work with leading economic researchers on influencing needle-moving business decisions using incomparable datasets and tools. It is an opportunity for PhD students in Economics or related fields. We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to work with large and complicated data sets. Knowledge of econometrics, as well as basic familiarity with Stata, R, or Python is necessary. Experience with SQL would be a plus. As an Economics Intern, you will be working in a fast-paced, cross-disciplinary team of researchers who are pioneers in the field. You will take on complex problems, and work on solutions that either leverage existing academic and industrial research, or utilize your own out-of-the-box pragmatic thinking. In addition to coming up with novel solutions and prototypes, you may even need to deliver these to production in customer facing products. Roughly 85% of previous intern cohorts have converted to full time scientist employment at Amazon.
Data Scientist, Prime Video Sports
IL, Tel Aviv
Prime Video is one of the world's fastest-growing entertainment destinations, with Live Sports at the center of that growth. From NFL and NBA to Premier League and Champions League, Prime Video is becoming a premier home for live sports globally. Our team owns the data science that shapes how tens of millions of customers discover and experience Sports content on Prime Video. We build measurement frameworks, experimentation systems, and analytical foundations powering Search, Recommendations, and the live broadcast experience itself. Sports presents uniquely exciting challenges: content is live and time-sensitive, customer intent shifts rapidly, and the difference between a great and poor experience is measured in minutes. We tackle problems like real-time search intent during live matches, sports engagement and long-term retention, broadcast optimization for global audiences, and personalization across vastly different affinities, from die-hard fans to first-time viewers. You'll work on large-scale experimentation, novel measurement methodologies, and data-driven decisions that directly shape the Prime Video customer experience. This is a ground-floor opportunity to define the data science practice for an expanding domain, from content discovery through the live viewing experience, partnering with Applied Scientists, Software Engineers, Product Managers, and Broadcasting Teams to turn insights into global product impact. Key job responsibilities - Experimentation at scale: Design, execute, and analyze A/B tests, from short-cycle learning experiments to full product launches across worldwide marketplaces - Measurement & metrics: Build frameworks to measure content discovery quality, search recall, recommendation relevance, and broadcast experience, including novel methodologies for live, time-sensitive content - Sports analytics: Deep-dive into customer behavior around live events: engagement patterns, affinity segmentation, broadcast quality, and tentpole event dynamics - Product partnership: Partner with Applied Scientists, Engineers, and Product Managers to define requirements, evaluate models, and drive data-informed decisions - Analytical leadership: Own data structures, metrics definitions, and best practices. Communicate findings clearly to technical and business stakeholders - Shape a growing domain: Help define the data science roadmap as we expand into new areas of live sports
Applied Scientist, Trustworthy Shopping Experience (TSE)
IN, KA, Bengaluru
Are you passionate about solving complex business problems at scale through Generative AI? Do you want to help build intelligent systems that reason, act, and learn from minimal supervision? If so, we have an exciting opportunity for you on Amazon's Trustworthy Shopping Experience (TSE) team. At TSE, our vision is to guarantee customers a worry-free shopping experience by earning their trust that the products they buy are safe, authentic, and compliant with regulations and policy. We do this in close partnership with our selling partners, empowering them with best-in-class tools and expertise to offer a high-quality, compliant selection that customers trust. As an Applied Scientist I, you will bring subject matter expertise in at least one relevant discipline (e.g., NLP, computer vision, representation learning, agentic architecture) to contribute to next-generation agentic AI solutions that automate complex manual investigation processes at Amazon scale. Working alongside senior scientists, you will map business goals—such as reducing cost-of-serving while maintaining trust and safety standards—to well-defined scientific problems and metrics. You will invent, refine, and experiment with solutions spanning agentic reasoning, self-supervised representation learning, few-shot adaptation, multimodal understanding, and model compression. With guidance from senior scientists, you will stay current on research trends and benchmark your results against the state of the art. You will help design and execute experiments to identify optimal solutions, initiating the development and implementation of small components with team guidance. You will write secure, stable, testable, and well-documented production code at the level of an SDE I, rigorously evaluating models and quantifying performance. You will handle data in accordance with Amazon policies, troubleshoot issues to root cause, and ensure your work does not put the company at risk. Your scope of influence will typically be at the self-level, with the possibility of mentoring interns. You will participate in team design and prioritization discussions, learn the business context behind TSE's products, and escalate problems with proposed solutions. You will publish internal technical reports and may contribute to peer-reviewed publications and external review activities when aligned with business needs. This role offers a unique opportunity to contribute to end-to-end AI development—from research through production—with your contributions serving hundreds of millions of customers within months, not years. Key job responsibilities • Contribute to the design and development of agentic AI systems with multi-step reasoning, autonomous task execution, and multimodal intelligence, including feedback and memory mechanisms, leveraging reinforcement learning techniques for agent decision-making and policy optimization, with input and guidance from senior scientists • Help productionize models built on top of SFT (Supervised Fine-tuning) and RFT (Reinforced Fine-tuning) approaches, as well as few-shot approaches based on multimodal datasets spanning text, images, and structured data, applying mathematical optimization techniques to improve efficiency, resource allocation, and decision-making in complex workflows, working alongside senior scientists to identify optimal solutions • Contribute to building production-ready deep learning and conventional ML solutions, including multimodal fusion and cross-modal alignment techniques that seamlessly connect visual, textual, and relational understanding, to support automation requirements within your team's scope • Help identify customer and business problems; use reasonable assumptions, data, and customer requirements to solve well-defined scientific problems involving multimodal inputs such as unstructured text, documents, product images, and relational data, developing representations that capture complementary signals across modalities and mapping business goals to scientific metrics • May co-author research papers for peer-reviewed internal and/or external venues, including contributions in areas such as multimodal representation learning and vision-language modeling, and contribute to the wider scientific community by reviewing research submissions, when aligned with business needs • Prototype rapidly, iterate based on feedback, and deliver small components at SDE I level—including multimodal data pipelines and inference modules—that integrate into production-scale systems • Write secure, stable, testable, maintainable, and well-documented code, balancing model capability, deployment cost, and resource usage across multimodal architectures while understanding state-of-the-art data structures, algorithms, and performance tradeoffs • Rigorously test code and evaluate models across individual and combined modalities, quantifying their performance; troubleshoot issues, research root causes, and thoroughly resolve defects, leaving systems more maintainable • Participate in team design, scoping, and prioritization discussions through clear verbal and written communication; seek to learn the business context, science, and engineering behind your team's products, including how multimodal signals contribute to trust and safety decisions • Participate in engineering best practices with peer reviews; clearly document approaches and communicate design decisions; publish internal technical reports to institutionalize scientific learning • Help train and mentor scientist interns; identify and escalate problems with proposed solutions, taking ownership or ensuring clear hand-off to the right owner About the team Trustworthy Shopping Experience Product team in TSE is responsible for the human-in-the-loop products and technology used in the risk investigations at Amazon. The team is also responsible for reducing the cost of performing the investigations, by automating wherever possible and optimizing the experience where manual interventions are needed. The team leverages state-of-the art technology and GenAI to deliver the products and associated goals.
Applied Scientist II, Advertising Incrementality Measurement
US, NY, New York
Do you want to lead the Ads industry and redefine how we measure the effectiveness of Amazon Ads business? Are you passionate about causal inference, Deep Learning & AI, raising the science bar, and connecting leading-edge science research to Amazon-scale implementation? If so, come join Amazon Ads to be a science leader within our Advertising Incrementality Measurement science team! Our work builds the foundations for providing customer-facing advertising measurement tools, furthering internal research & development, and building out Amazon's advertising measurement offerings. Incrementality is a lynchpin for the next generation of Amazon Advertising measurement solutions, and this role will play a key role in the release and expansion of these offerings. We are looking for a thought leader that has an aptitude for delivering customer-focused solutions and who enjoys working on the intersection of Big-Data analytics, Machine/Deep Learning, and Causal Inference. A successful candidate will be a self-starter, comfortable with ambiguity, able to think big and be creative, while still paying careful attention to detail. You should be able to translate how data represents the customer journey, be comfortable dealing with large and complex data sets, and have experience using machine learning and/or econometric modeling to solve business problems. You should have strong analytical and communication skills, be able to work with product managers to define key business questions and work with the engineering team to bring our solutions into production. You will join a highly collaborative and diverse working environment that will empower you to shape the future of Amazon advertising, and also allow you to become part of our large science community. Key job responsibilities • Apply expertise in ML/DL, AI, and causal modeling to develop new models that describe how advertising impacts customers’ actions • Own the end-to-end development of novel scientific models that address the most pressing needs of our business stakeholders and help guide their future actions • Improve upon and simplify our existing solutions and frameworks • Review and audit modeling processes and results for other scientists, both junior and senior • Work with leadership to align our scientific developments with the business strategy • Identify new opportunities that are suggested by the data insights • Bring a department-wide perspective into decision making • Develop and document scientific research to be shared with the greater science community at Amazon About the team AIM is a cross disciplinary team of engineers, product managers, economists, data scientists, and applied scientists with a charter to build scientifically-rigorous causal inference methodologies at scale. Our job is to help customers cut through the noise of the modern advertising landscape and understand what actions, behaviors, and strategies actually have a real, measurable impact on key outcomes. The data we produce becomes the effective ground truth for advertisers and partners making decisions affecting millions in advertising spend.
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