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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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Member of Technical Staff, Artificial General Intelligence
US, CA, Sunnyvale
The Artificial General Intelligence (AGI) team is looking for a passionate, talented, and inventive Member of Technical Staff with a strong deep learning background, to build industry-leading Generative Artificial Intelligence (GenAI) technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As a Member of Technical Staff with the AGI team, you will support the development of algorithms and modeling techniques, to advance the state of the art with LLMs. You will support the foundational model development in an applied research role, including model training, dataset design, and pre- and post-training optimization. Your work will directly impact our customers in the form of products and services that make use of GenAI technology. You will leverage Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in LLMs. About the team The AGI team has a mission to push the envelope in GenAI with LLMs and multimodal systems, in order to provide the best-possible experience for our customers.
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.
Manager, Data Science, Outbound Communications
US, WA, Seattle
Are you passionate about using data science and machine learning to optimize how hundreds of millions of customers experience communications from the world's most customer-centric company? Join the Outbound Communications Intelligence team at Amazon, where you will lead the development of scalable/robust advanced AI based methods like LLMs and RL to personalize the relevance, frequency and timing of messages across push, email, WhatsApp, and SMS channels reaching 250M+ global customers every week. You will lead the insights arm to build highly accurate and world-class self-service analytics solutions that guide the short- and long-term investments for the business. Key job responsibilities You will lead applied scientists, data scientists and business intelligence engineers to: - Optimize Outbound's inbox management and planning system to personalize frequency, send-time and relevance bar of our messages to customers. - Design and execute large-scale experiments such as multi-arm elasticity tests or RCTs to measure and improve incrementality/performance of our models. - Drive development of HVA propensity models (opt-out, purchase, etc.) to drive intended behavior of customers to their next stage of shopping and engagement with Amazon. - Drive AI-based transformation in data accuracy and reporting: migrating and enhancing the self-serve analytics capabilities developed by the team, automating WBR preparation, building anomaly detection, etc. - Own financial planning frameworks for outbound performance including QxG/HVE forecasting and ROI measurement for paid channel investments. In addition, you will: - Hire, develop, and mentor scientists and BIEs while partnering cross-functionally with engineering, product, marketing, and partner science teams (CBA, P13N, CFV) to productionize solutions at scale. - Create, align and evolve your team's roadmap by prioritizing across multiple competing priorities using high judgement decisions.
Applied Scientist, Ads Measurement Science
US, NY, New York
The Ads Measurement Science team in the Measurement, Ad Tech, and Data Science (MADS) team of Amazon Ads serves a centralized role developing solutions for a multitude of performance measurement products. We create solutions which measure the comprehensive impact of advertiser's ad spend, including sales impacts both online and offline and across timescales, and provide actionable insights that enable our advertisers to optimize their media portfolios. We also own the science solutions for AI tools that unlock new insights and automate high-effort customer workflows, such as custom query and report generation based on natural language user requests. We leverage a host of scientific technologies to accomplish this mission, including Generative AI, classical ML, Causal Inference, Natural Language Processing, and Computer Vision. As an Applied Scientist on the team, you will lead measurement solutions end-to-end from inception to production. You will propose, design, analyze, and productionize models to provide novel measurement insights to our customers. Key job responsibilities - Leverage deep expertise in one or more scientific disciplines to invent solutions to ambiguous ads measurement problems - Disambiguate problems to propose clear evaluation frameworks and success criteria - Work autonomously and write high quality technical documents - Implement a significant portion of critical-path code, and partner with engineers to directly carry solutions into production - Partner closely with other scientists to deliver large, multi-faceted technical projects - Share and publish works with the broader scientific community through meetings and conferences - Communicate clearly to both technical and non-technical audiences - Contribute new ideas that shape the direction of the team's work - Mentor more junior scientists and participate in the hiring process About the team We are a team of scientists across Applied, Research, Data Science and Economist disciplines. You will work with colleagues with deep expertise in ML, NLP, CV, Gen AI, and Causal Inference with a diverse range of backgrounds. We partner closely with top-notch engineers, product managers, sales leaders, and other scientists with expertise in the ads industry and on building scalable modeling and software solutions.
Applied Scientist, Ads Measurement Science
US, NY, New York
The Ads Measurement Science team in the Measurement, Ad Tech, and Data Science (MADS) team of Amazon Ads serves a centralized role developing solutions for a multitude of performance measurement products. We create solutions which measure the comprehensive impact of advertiser's ad spend, including sales impacts both online and offline and across timescales, and provide actionable insights that enable our advertisers to optimize their media portfolios. We also own the science solutions for AI tools that unlock new insights and automate high-effort customer workflows, such as custom query and report generation based on natural language user requests. We leverage a host of scientific technologies to accomplish this mission, including Generative AI, classical ML, Causal Inference, Natural Language Processing, and Computer Vision. As an Applied Scientist on the team, you will lead measurement solutions end-to-end from inception to production. You will propose, design, analyze, and productionize models to provide novel measurement insights to our customers. Key job responsibilities - Leverage deep expertise in one or more scientific disciplines to invent solutions to ambiguous ads measurement problems - Disambiguate problems to propose clear evaluation frameworks and success criteria - Work autonomously and write high quality technical documents - Implement a significant portion of critical-path code, and partner with engineers to directly carry solutions into production - Partner closely with other scientists to deliver large, multi-faceted technical projects - Share and publish works with the broader scientific community through meetings and conferences - Communicate clearly to both technical and non-technical audiences - Contribute new ideas that shape the direction of the team's work - Mentor more junior scientists and participate in the hiring process About the team We are a team of scientists across Applied, Research, Data Science and Economist disciplines. You will work with colleagues with deep expertise in ML, NLP, CV, Gen AI, and Causal Inference with a diverse range of backgrounds. We partner closely with top-notch engineers, product managers, sales leaders, and other scientists with expertise in the ads industry and on building scalable modeling and software solutions.
Machine Learning Scientist / Applied Scientist, EU Prime and Marketing Analytics & Science (PRIMAS)
ES, B, Barcelona
Are you interested in changing how Amazon does marketing — moving beyond platform-optimized broad reach to campaigns that find the right customer, at the right moment, using Amazon's unmatched 1P data? We are seeking an Applied Scientist to join PRIMAS (Prime & Marketing Analytics and Science). In this role, you will design and run the experiments that answer the foundational question for EU marketing: does adding 1P audience signal on top of Value-Based Optimization (VBO) improve marketing efficiency — and if so, for which customer cohorts, on which surfaces, and at what scale? Amazon's current marketing model is largely platform-led: we set objectives and let platforms optimize toward conversion. This approach works well for broad acquisition but systematically underserves lifecycle goals — it cannot distinguish between a Bargain Hunter who will never pay full price and a high-potential customer one nudge away from becoming a Prime member. This role sits at the center of changing that. You will build the 1P audiences, design the experiments that test them, and generate the evidence that guides how Amazon allocates hundreds of millions in marketing spend. Year 1 is an experimentation year. You will deploy 1P audiences across multiple surfaces and channels — Meta, Google, Amazon Display Ads — and measure incrementally against VBO baselines. The goal is not to replace platform optimization but to understand when and where the combination of 1P signal + VBO outperforms VBO alone, and to build the experimental infrastructure that makes this learning scalable. Key job responsibilities 1P Audience Development & Experimentation: - Build and validate 1P audience segments from Amazon behavioral, transactional, and lifecycle data - Design experiments that isolate the incremental effect of 1P audience signal over platform VBO baselines - Deploy audiences across activation surfaces and establish measurement standards that make cross-surface comparison valid Causal Measurement & Incrementality: - Apply causal inference methods to measure the true incremental lift of audience-based targeting vs. VBO - Develop power analysis frameworks and guardrails that enable rapid experimentation without underpowered or conflated tests - Deliver optimization recommendations grounded in experimental evidence: which cohorts respond, which surfaces deliver, which creative strategies drive behavior change Scaling the Learning: - Build reusable audience and measurement frameworks that can be deployed across campaigns and channels — year 1 experiments should produce infrastructure, not one-off analyses - Document experimental learnings in a way that informs both the 2026 roadmap and the business case for investing further in 1P audience capabilities in 2027+ - Partner with engineering and PMT to translate validated audience prototypes into production-ready solutions that scale beyond the experimentation phase About the team The PRIMAS team, is part of a larger tech tech team of 100+ people called WIMSI (WW Integrated Marketing Systems and Intelligence). WIMSI core mission is to accelerate marketing technology capabilities that enable de-averaged customer experiences across the marketing funnel: awareness, consideration, and conversion.
Technical Lab Manager - Robotics R&D, Frontier AI & Robotics
US, CA, San Francisco
Join our Frontier AI & Robotics team to lead the hardware integration of next-generation robotic systems that will transform how robots perceive and interact with the world. You'll take ownership of critical hardware components, from advanced actuators to precision sensors, ensuring they work seamlessly together to support breakthrough AI research and real-world deployment. Key job responsibilities - Prototype Lab Leadership — Lead & develop a cross-functional technician team supporting robotic prototype hardware; own daily priorities, team KPIs, and risk communication to FAR leadership. Serve as the technical escalation point for the lab. - Assembly, Integration & DFx ownership — Assemble & integrate robotic hardware (actuators, sensors, vision, machined components). Build assembly processes and test protocols with hardware engineering. Drive DFM/DFA feedback and own simple mechanical/electrical design tasks, lead integration/debug, and partner with engineers to optimize manufacturability and testability. - Own R&D prototype test & validation — Validate hardware revisions, verify mechanical assemblies, power sequencing, comms interfaces, and peripherals during bring-up. - Build a strong debugging & failure analysis function — Troubleshoot & root-cause across the full robot platform (power, compute, comms, actuators, sensors); hands-on for complex issues, directing the team on routine ones. Conduct failure analysis from component to system level using oscilloscopes, logic analyzers, and multimeters; train technicians on diagnostic techniques. Reproduce critical failures, interpret schematics, and bridge communication between the lab and engineering teams. - Own lab technical documentation — Own documentation & quality - author runbooks, FA reports, assembly guides and troubleshooting guides; mentor the team to maintain consistent standards. - Own mechanical design for the lab — Own mechanical design technician output. Oversee technicians performing simple R&D design tasks and test fixture design, ensuring quality and alignment with engineering priorities. - Manage prototyping lab operations — oversee machine shop capabilities and quality, equipment/inventory, vendor coordination, and safety/regulatory compliance. - Build additional lab capabilities — develop test methodologies, design jigs/fixtures, implement HIL testing, and streamline failure-to-resolution workflows. A day in the life Your focus centers on the hardware that powers our advanced robotic platforms. You'll lead a strong robotics technician and lab engineering team to support high degrees of freedom (DoF) robotic hardware prototype assembly and validation. Your team will be responsible for building, debugging and validating prototype hardware, critical component and assembly quality assessments, providing DFM/DFA feedback to engineers and designing test jigs and test set-ups. You’ll manage responsibilities like quality inspections of incoming parts, one-on-ones with technicians, and coordinating machine shop operations. Throughout the day, you balance leading your team through complex assemblies and integration testing while also handling urgent prototyping requests, documentation updates, and planning for upcoming milestones. You're switching between working at the bench alongside your technicians, collaborating in design reviews with engineers, and ensuring lab safety and equipment maintenance. About the team At Frontier AI & Robotics, we're not just advancing robotics – we're reimagining it from the ground up. Our team is building the future of intelligent robotics through frontier foundation models and end-to-end learned systems. We tackle some of the most challenging problems in AI and robotics, from developing sophisticated perception systems to creating adaptive manipulation strategies that work in complex, real-world scenarios. What sets us apart is our unique combination of ambitious research vision and practical impact. We leverage Amazon's computational infrastructure and rich real-world datasets to train and deploy state-of-the-art foundation models. Our work spans the full spectrum of robotics intelligence – from multimodal perception using images, videos, and sensor data, to sophisticated manipulation strategies that can handle diverse real-world scenarios. We're building systems that don't just work in the lab, but scale to meet the demands of Amazon's global operations. Join us if you're excited about pushing the boundaries of what's possible in robotics, working with world-class researchers, and seeing your innovations deployed at unprecedented scale.
Data Scientist II, Enterprise Security Products
US, TX, Austin
What happens when you combine startup speed with Amazon-scale impact? You get this team. Amazon Enterprise Security Products is a newly launched group building intelligent, cloud-agnostic security tools using AI-first development practices. Here, you build AI and you build with AI — at the same time. This role is a chance to shape the future of security tooling with a small, fast team that ships like a startup but deploys at Amazon scale. We're looking for a Data Scientist who thrives at the intersection of applied ML, agentic AI, and security. You'll design and deploy models that detect threats, power intelligent agents, and make security decisions at cloud scale. You'll work shoulder-to-shoulder with SDEs, applied scientists, security researchers, and PMs on a team where the best idea wins, regardless of title or tenure. Key job responsibilities * Build the intelligence behind AI-first security products: Design, train, and ship ML models that power agentic systems, anomaly detection, threat classification, and automated response — all running across multi-cloud environments. * Own the full science lifecycle: From problem framing and data exploration through model development, evaluation, production deployment, and monitoring. You build it, you ship it, you run it. * Build with AI to build AI: Use agentic coding tools, LLM-powered workflows, and experimental AI tooling to accelerate every phase of your work; from EDA to feature engineering to model iteration. Multiply your velocity and raise the bar for what one scientist can deliver. * Power agentic architectures: Develop the models, embeddings, RAG pipelines, evaluation frameworks, and feedback loops that make multi-agent security systems smart, safe, and customer-ready. * Prototype rapidly and validate with customers: Turn hypotheses into prototypes in days, not quarters. Iterate based on real customer signal and ship what works. * Partner across disciplines: Work directly with SDEs, applied scientists, security researchers, PMs, and UX designers to turn ambiguous problems into shipped solutions. Small team means short lines between you and the decision. * Communicate with impact: Translate complex modeling results into clear recommendations for engineers, product leaders, and senior executives. Influence direction with data. * Raise the science bar: Contribute to technical and science reviews, mentor teammates, and champion AI-first development practices. Help shape the science culture of a fast-growing team from the ground floor. A day in the life No two days look the same on this fast-growing, AI-first team. You might start your morning reviewing evaluation results from overnight model training runs, then dive into building a RAG pipeline or tuning a multi-agent orchestration loop. Before lunch, you're pair-prompting with an agentic coding assistant to stand up a new feature pipeline. In the afternoon, you join a design session with senior and principal scientists and engineers where your ideas carry weight regardless of title. You own science problems end to end, ship using the latest AI-assisted workflows, and see your models reach production fast. This is where builders thrive. About the team Amazon Enterprise Security Products is built by builders who tackle challenges others might consider too ambitious. We're a small team where there are no layers between you and the decision, no waiting quarters to see your work reach customers. Every team member brings an owner's mentality. If there's a problem worth solving, we solve it. No mission is beyond reach, no detail beneath our attention. We move fast, we ship fast, and we learn from what we ship. This is where builders who want to make the impossible routine come to do their best work. 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.
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