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Automated Reasoning call for proposals — Fall 2022

Systems assurance by mathematical proof

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About this CFP

Amazon Science 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 provides higher assurance for customers—both about the cloud itself and about their data in the cloud. We anticipate funding research in these categories:

  • Software verification, e.g., C, Rust, Java
  • Dafny
  • Improvements to SAT, SMT, and CHC solvers
  • Model checking
  • Correct-by-construction software
  • Novel applications of automated reasoning
  • Theorem proving
  • Abstract interpretation
  • Static analysis
  • Efficient reasoning with quantifiers
  • Automated reasoning

Timeline

Submission period: September 16 to October 26, 2022

Decision letters will be sent out March 2023.

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

Awards 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 FAQ 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 and industry code.)
  3. What are potential applications of your work?
  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. What license is your tool released under?
    2. What on-boarding/tutorial material is available?
    3. Is your tool actively maintained (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 Amazon, 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 students working on automated reasoning at the undergraduate and graduate levels. 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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Sr. Manager, Applied Science, AWS Agentic AI
US, WA, Seattle
Amazon Web Services (AWS) is looking for a sr. Manager, Applied to join the Quick Science team. Quick is AWS’s enterprise generative AI assistant that helps users answer questions, summarize documents, generate content, take actions, and automate workflows using information across enterprise systems. As a key member of this team, you will lead research and development efforts in generative AI and Agentic AI to enable intelligent agents that perform complex reasoning, automate multi-step workflows, and make enterprise users significantly more productive. Key job responsibilities You’ll work on building and optimizing multi-modal foundation models, training and fine-tuning state-of-the-art LLMs, and architecting systems that scale efficiently across domains. This role blends science leadership, development of applied scientists, innovation, and deep collaboration with engineering teams to bring research into production.
Sr. Applied Scientist, Sponsored Products and Brands Agent
US, NY, New York
The Sponsored Products and Brands team at Amazon Ads is re-imagining the advertising landscape through 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. About the team SPB Agent team's vision is to build a highly personalized and context-aware agentic advertiser guidance system that seamlessly integrates Large Language Models (LLMs) with sophisticated tooling, operating across all experiences. The SPB-Agent is the central agent that interfaces with advertisers across Ads Console, Selling Partner portals (Seller Central, KDP, Vendor Central), and internal Sales systems. We identify high-impact opportunities spanning from strategic product guidance to granular optimization and deliver them through personalized, scalable experiences grounded in state-of-the-art agent architectures, reasoning frameworks, sophisticated tool integration, and model customization approaches including fine-tuning, MCP, and preference optimization. This presents an exceptional opportunity to shape the future of e-commerce advertising through advanced AI technology at unprecedented scale, creating solutions that directly impact millions of advertisers.
Applied Scientist, Industry Builder Team
CN, 13, Beijing
About Amazon Web Services Since 2006, Amazon Web Services has been the world’s most comprehensive and broadly adopted cloud. AWS has been continually expanding its services to support virtually any workload, and it now has more than 240 fully featured services for compute, storage, databases, networking, analytics, machine learning and artificial intelligence (AI), Internet of Things (IoT), mobile, security, hybrid, media, and application development, deployment, and management from 105 Availability Zones within 33 geographic regions, with announced plans for 18 more Availability Zones and 6 more AWS Regions in Malaysia, Mexico, New Zealand, the Kingdom of Saudi Arabia, Thailand. Millions of customers—including the fastest-growing startups, largest enterprises, and leading government agencies—trust AWS to power their infrastructure, become more agile, and lower costs. To learn more about AWS, visit aws.amazon.com. AWS Global Sales drives adoption of the AWS cloud worldwide, enabling customers of all sizes to innovate and expand in the cloud. Our team empowers every customer to grow by providing tailored service, unmatched technology, and consistent support. We dive deep to understand each customer's unique challenges, then craft innovative solutions that accelerate their success. This customer-first approach is how we built the world's most adopted cloud. Join us and help us grow. Amazon Web Services came to China in 2013, and has been relentlessly investing and expanding our infrastructure and business since then. Amazon Web Services launched its China (Beijing) Region (operated by Sinnet) in September 2016 and its China (Ningxia) Region (operated by NWCD) in December 2017. In 2019, Amazon Web Services added a new region in Hong Kong, making China the only country with three Amazon Web Services regions aside from the U.S. In 2022, Amazon Web Services launched Local Zone in Taipei. Amazon Web Services has also established an AI lab in Shanghai and two IoT labs in Shenzhen and Taipei. The Amazon Web Services Partner Network has thousands of Partners in China. Amazon Web Services has supported over 10,000 local startups and has provided cloud skills training to over 700,000 talents. Amazon’s first two utility-scale renewable projects—a solar farm and a wind farm—are also generating clean energy to the country’s grid. Amazon Web Services (AWS) is looking for an Applied Scientist to join the Industry Builder Team in China Mainland & Hong Kong (CMHK). The Industry Builder Team designs and builds industry assets and capabilities: reusable solution accelerators, reference architectures, prototypes, and technical building blocks that address the highest-value use cases within a target industry (e.g., games, manufacturing, automotive, retail, media & entertainment). As an Applied Scientist on this team, you are an Industry Builder who owns the AI/ML core of these industry assets. You will invent, implement, and productize generative AI and machine learning components that make our industry assets differentiated, scalable, and ready for customers to adopt. You own the AI portion of the industry asset lifecycle end to end. You do not just prototype: you build assets, take them through review so they are validated for reuse, and you are accountable for the quality, documentation, and adoption of your assets. Because the team is measured by the business impact its assets generate, building assets that are reusable, well-documented, and adopted by the field is a core part of the role. This is a builder role at the intersection of science and industry. You will partner with industry specialists, solutions architects, product teams, and partners to turn industry problems into concrete, deployable AI capabilities, then harden them into assets the field can reuse across many customers. Key job responsibilities • Own the AI/ML and generative AI components of industry assets and capabilities: research, design, implement, and productize state-of-the-art models, agents, and pipelines that power reusable industry solutions • Own the full lifecycle of your assets: build them, take them through review so they are validated for reuse, document them well, and iterate based on field and customer feedback. Keep your assets' documentation and information complete and current so they are discoverable, reusable by the field, and their business impact can be accurately tracked • Translate prioritized industry use cases into technical building blocks (solution accelerators, reference implementations, sample code, and prototypes) that the field and partners can adopt at scale. Collaborate with industry specialists to shape the industry solution roadmap, contributing the science point of view on which AI capabilities are feasible, differentiated, and high-impact • Build rapid prototypes and minimum viable solutions in real customer engagements, then generalize the learnings into reusable assets. Apply generative AI techniques (prompt engineering, RAG, agentic workflows, fine-tuning, model hosting and deployment) and classical ML where appropriate to solve never-before-solved industry problems • Create technical collateral (best-practice guides, tutorials, blog posts, sample code, workshops, and presentations) adapted to technical, business, and executive stakeholders, and evangelize assets to field teams and customers About the team The Industry Builder Team focuses on industry solution innovation. We explore potential solutions and develop them into code assets to help empower our industry customers. Most of our solutions leverage agentic AI technology and are built on top of AWS cloud service building blocks. Diverse Experiences AWS values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Why AWS? Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Inclusive Team Culture AWS values curiosity and connection. Our employee-led and company-sponsored affinity groups promote inclusion and empower our people to take pride in what makes us unique. Our inclusion events foster stronger, more collaborative teams. Our continual innovation is fueled by the bold ideas, fresh perspectives, and passionate voices our teams bring to everything we do. 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.
Senior Applied Scientist, Delivery Foundation Model
US, CA, Santa Clara
Join the next science and engineering revolution at Amazon's Delivery Foundation Model team, where you'll work alongside world-class scientists and engineers to pioneer the next frontier of logistics through advanced AI and foundation models. We are seeking an exceptional Senior Applied Scientist to help develop innovative foundation models that enable delivery of billions of packages worldwide. In this role, you'll combine highly technical work with scientific leadership, ensuring the team delivers robust solutions for dynamic real-world environments. Your team will leverage Amazon's vast data and computational resources to tackle ambitious problems across a diverse set of Amazon delivery use cases. Key job responsibilities • Design and implement novel deep learning architectures combining a multitude of modalities, including image, video, and geospatial data. • Solve computational problems to train foundation models on vast amounts of Amazon data and infer at Amazon scale, taking advantage of latest developments in hardware and deep learning libraries. • As a foundation model developer, collaborate with multiple science and engineering teams to help build adaptations that power use cases across Amazon Last Mile deliveries, improving experience and safety of a delivery driver, an Amazon customer, and improving efficiency of Amazon delivery network. • Guide technical direction for specific research initiatives, ensuring robust performance in production environments. • Mentor fellow scientists while maintaining strong individual technical contributions. A day in the life • Develop and implement novel foundation model architectures, working hands-on with data and our extensive training and evaluation infrastructure • Guide and support fellow scientists in solving complex technical challenges, from trajectory planning to efficient multi-task learning • Guide and support fellow engineers in building scalable and reusable infra to support model training, evaluation, and inference • Lead focused technical initiatives from conception through deployment, ensuring successful integration with production systems- Drive technical discussions within the team and and key stakeholders • Conduct experiments and prototype new ideas • Mentor team members while maintaining significant hands-on contribution to technical solutions About the team The Delivery Foundation Model team combines ambitious research vision with real-world impact. Our foundation models provide generative reasoning capabilities required to meet the demands of Amazon's global Last Mile delivery network. We leverage Amazon's unparalleled computational infrastructure and extensive datasets to deploy state-of-the-art foundation models to improve the safety, quality, and efficiency of Amazon deliveries. Our work spans the full spectrum of foundation model development, from multimodal training using images, videos, and sensor data, to sophisticated modeling strategies that can handle diverse real-world scenarios. We build everything end to end, from data preparation to model training and evaluation to inference, along with all the tooling needed to understand and analyze model performance. Join us if you're excited about pushing the boundaries of what's possible in logistics, working with world-class scientists and engineers, and seeing your innovations deployed at unprecedented scale.
【Class of 2028／Full-Time】Applied Scientists , Amazon International Stores, Amazon Japan Store Tech
JP, 13, Tokyo
Do you want to see your research directly impact how millions of customers discover, browse, and purchase products on Amazon — across Japan and the globe? Amazon's Japan Store Tech team owns the science and technology behind cross-border shopping — product discovery, search relevance, personalization, and content experiences spanning dozens of marketplaces. We tackle problems at massive scale: multi-language signals, multi-marketplace data, and region-specific customer behaviors, all served at low latency to millions of daily shoppers. We're looking for current Bachelor or Master students with a passion for applied science and machine learning to join us as an Applied Scientist in 2028 to shape the future of customer experiences at scale. For this position, our Japan Store Tech team is looking for students with a specialization in one or more of the following research areas: machine learning, deep learning, natural language processing (NLP), information retrieval, recommender systems, computer vision, large language models (LLMs), generative AI, causal inference, experimentation and A/B testing, optimization, and more! As an Applied Scientist Intern, you'll develop novel models and algorithms, design and run experiments on live traffic, and own meaningful science contributions end-to-end. You'll also leverage and contribute to GenAI/LLM systems that power both customer-facing experiences and internal development tools. If you want to kickstart your science career at global scale — solving real customer problems alongside talented scientists and engineers in a collaborative, international environment — this is the place to start. Key job responsibilities - Collaborate and communicate effectively with experienced cross-disciplinary Amazonians to design, develop, and deploy innovative machine learning models and scientific solutions that delight our customers, while participating in technical discussions to drive solutions forward. - Develop and implement scalable machine learning models and algorithms to improve product discovery, search relevance, personalization, or other customer-facing experiences. - Design and conduct experiments (offline and online) to validate hypotheses and measure the impact of proposed solutions. - Analyze large-scale datasets to identify patterns, generate insights, and inform model design decisions. - Leverage and contribute to the development of GenAI and LLM-powered tools to enhance customer experiences and development productivity while staying current with emerging technologies. - Write clean, maintainable, production-quality code following best practices. - Communicate research findings effectively through documentation, presentations, and technical papers. - Work in an agile environment and collaborate closely with software engineers to bring science solutions from prototype to production.
Applied Scientist III, Sponsored Products and Brands - Advertiser Growth and Strategies
US, WA, Seattle
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. We are looking for an Applied Scientist III to set the scientific direction for the next generation of agentic AI applications that guide Amazon advertisers. In this role you will define, lead and build the science behind agentic systems that reason, plan, and act autonomously to manage and optimize ad campaigns based on a deep understanding of the advertiser and the marketplace. You will own the agentic architecture end to end, partnering closely with product and engineering leaders to translate a long-term science vision into concrete research and engineering roadmaps. Working backwards from the needs of millions of advertisers, you will take the lead on medium-to-large, ambiguous problems where neither the problem nor the solution is well defined, and deliver customer-facing products that help advertisers create, optimize, and grow their campaigns. You will invent new methods at the product level, and drive their adoption across multiple teams. This role combines science leadership, technical depth, product focus, and business understanding: you will raise the science bar, build consensus on approach across partners, and mentor scientists and engineers while remaining deeply hands-on with the hardest technical problems. Key job responsibilities As an Applied Scientist III on this team you will: - Define the science vision for the agentic campaign management system and, with product and engineering leaders, turn it into delivery roadmaps. - Build agentic systems that autonomously manage and optimize ad campaigns — encoding auction and marketplace dynamics (bidding, budget pacing, keyword and targeting decisions) while balancing advertiser ROI, shopper experience, and marketplace health. - Define and curate the datasets and signals needed to train and evaluate these agents — advertiser and campaign data, auction and bid/budget signals, impressions, clicks, conversions, and search-term/keyword performance. - Stay deeply hands-on: write production-quality, critical-path code and build core components that take agentic systems from prototype to launch. - Own the agentic architecture — planning, tool use and integration (e.g., MCP), long-horizon reasoning (e.g., ReAct, CoT/ToT), and multi-agent orchestration — and stay deeply hands-on, writing production-quality, critical-path code from prototype to launch. - Define the evaluation and safety methodology for agent workflows and drive its adoption as the bar for reliability and trust. - Drive the team's scientific agenda, mentor scientists and engineers, and represent the team in the internal and external scientific community. About the team The Sponsored Products and Brands team at Amazon Ads is re-imagining the advertising landscape through the latest 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. This team within Sponsored Products and Brands is focused on guiding and supporting millions of advertisers to meet their advertising needs of creating and managing ad campaigns. At this scale, the complexity of diverse advertiser goals, campaign types, and market dynamics creates both a massive technical challenge and a transformative opportunity: even small improvements in guidance systems can have outsized impact on advertiser success and Amazon’s retail ecosystem. Our vision is to build a highly personalized, context-aware agentic advertiser guidance system that leverages LLMs together with tools such as auction simulations, ML models, and optimization algorithms. This agentic framework, will operate across both chat and non-chat experiences in the ad console, scaling to natural language queries as well as autonomously manage campaigns based on deep understanding of the advertiser. To execute this vision, we collaborate closely with stakeholders across Ad Console, Sales, and Marketing to identify opportunities—from high-level product guidance down to granular keyword recommendations—and deliver them through a tailored, personalized experience. Our work is grounded in state-of-the-art agent architectures, tool integration, reasoning frameworks, and model customization approaches (including tuning, MCP, and preference optimization), ensuring our systems are both scalable and adaptive.
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