Careers

The Agentic Automated Reasoning Group is building the next generation of software verification tools combining advances in artificial intelligence, the computational capacity of the cloud, and our deep expertise in the domain. Join us if you want to be a part of this transformational endeavor. The Strata team (https://github.com/strata-org) is seeking a Principal Applied Scientist with broad interest and expertise in interactive theorem proving, programming language semantics, deductive verification and generative AI. You will combine your expertise with that of your coworkers to build new tools that solve code analysis problems previously considered beyond reach. Our application areas span all the way from Infrastructure as Code to high-performance cryptography written in assembly code, while our methods span from interactive theorem proving to automated test generation. Each day, hundreds of thousands of developers make billions of transactions worldwide on AWS. They harness the power of the cloud to enable innovative applications, websites, and businesses. Using automated reasoning technology and mathematical proofs, AWS allows customers to answer questions about security, availability, durability, and functional correctness. We call this provable security, absolute assurance in security of the cloud and in the cloud. https://aws.amazon.com/security/provable-security/ Key job responsibilities - Define roadmap and lead delivery of AR solutions across multiple customer use cases. - Identify tools and methods capable of addressing the verification needs of customers, including any novel analysis capabilities required. - Use tools spanning from fuzzers, property-based testing to model checkers, and interactive theorem provers to establish program properties. - Explore generative AI techniques to help customers formalize their requirements, find revealing tests, generate required boiler plate for testing and model checking, and find and repair program proofs. About the team You will be working with a team of formal verification specialists spanning recently hired PhDs to industry veterans. You will work collaboratively to deliver results in the form of verified code and tools to accelerate code verification for our customer teams.

The Amazon Center for Quantum Computing in Pasadena, CA, is looking to hire an Applied Scientist on the Materials team, focused on understanding and mitigating materials-driven loss mechanisms in superconducting quantum processors. You will join a multi-disciplinary team of theoretical and experimental physicists, materials scientists, and hardware and software engineers working at the forefront of quantum computing. You should have deep expertise in materials characterization and computational modeling of disordered solids, with the ability to connect atomic-scale insights to materials design. Candidates with a track record of original scientific contributions in experimental and computational studies of materials defects will be preferred. We are looking for candidates with strong scientific and engineering principles, resourcefulness and a bias for action, superior problem solving, and excellent communication skills. As an Applied Scientist at CQC, you will be expected to drive materials research from characterization through design recommendations and stay abreast of advances in materials science for superconducting quantum hardware. Key job responsibilities You will combine materials characterization and numerical simulations to investigate how material defects affect qubit performance. This includes implementing multi-technique characterization workflows for thin films and interfaces, providing input on the design of materials with targeted properties, and developing computational tools for simulations of disordered structures. You will provide characterization support for the Fabrication team, investigating materials sources of loss in production-relevant films and processes. You will coordinate with cross-functional teams to translate materials insights into actionable process improvements, and publish results in scientific journals when appropriate.

We are seeking an experienced Data Scientist to drive scientific tooling supporting how Amazon's business customers interact with LTPF forecasts and plans. As a science leader within the LTPF, you will be responsible for building to the multi-year roadmap for customer engagement, ensuring that business stakeholders across Amazon can seamlessly access, understand, and act upon our forecasting outputs. In this role, you will manage the lifecycle of complex, cross-functional programs that transform how Operations, Stores, and Finance teams leverage LTPF insights for strategic decision-making. You will work with scientists, economists, engineers, and business customers to architect the customer interaction experience, including viewing capabilities, auditing tools, what-if analysis frameworks, and forecast intervention workflows. This role might be for you if you have interest and experience in: - Leading large, cross-functional planning and strategy workstreams that impact Amazon's topline growth - Defining multi-year program vision and strategy while balancing short-term execution - Regularly presenting to VP and SVP level leaders - Prioritizing operational excellence work alongside feature delivery on a roadmap - Showing strong business acumen with strategic, analytical, and critical thinking - Managing planning calendars and strategic review mechanisms - Driving organizational alignment across multiple teams and stakeholders Key job responsibilities As a Data Scientist in LTPF (Long-Term Planning & Forecasting): - You will develop causal inference models, automated explainability frameworks, and variance bridging methodologies that translate LTPF's forecasts and plans into actionable business intelligence. - Your work will enable leadership to understand why forecasts and actuals diverge, what is driving demand shifts, and how strategic decisions propagate through the planning ecosystem. - You will build automated Plan-vs-Actual and Actual-vs-Actual variance decomposition models that quantify the contribution of individual demand drivers to observed gaps across revenue, price, units, inventory, and capacity metrics at multiple granularities to serve audiences from working-level analysts to VP-level planning reviews cycles. - You will build and maintain a causal model library with standardized hypothesis generation and validation pipelines, applying techniques from causal inference, time-series econometrics, and Bayesian methods. Each model will include calibrated confidence scoring and reusable components that scale across worldwide marketplaces. - You will develop GenAI-powered narrative generation capabilities that synthesize quantitative variance outputs into human-readable performance summaries and design automated hypothesis ranking to determine which demand drivers are most responsible for observed forecast error. A day in the life Amazon offers a full range of benefits that support you and eligible family members, including domestic partners and their children. Benefits can vary by location, the number of regularly scheduled hours you work, length of employment, and job status such as seasonal or temporary employment. The benefits that generally apply to regular, full-time employees include: - Medical, Dental, and Vision Coverage - Maternity and Parental Leave Options - Paid Time Off (PTO) - 401(k) Plan If you are not sure that every qualification on the list above describes you exactly, we'd still love to hear from you! At Amazon, we value people with unique backgrounds, experiences, and skillsets. If you’re passionate about this role and want to make an impact on a global scale, please apply! About the team The Long-Term Planning and Forecasting (LTPF) organization is dedicated to answering some of Amazon's most important strategic questions: Where will Amazon's growth come from in the next year? What about over the next five years? Which product lines are poised to grow significantly? Are we investing appropriately in our infrastructure? How do our customers react to changes in prices, product selection, or delivery times? Are our infrastructure investments optimal for the level of demand we expect?

Amazon Music is an immersive audio entertainment service that deepens connections between fans, artists, and creators. From personalized music playlists to exclusive podcasts, concert livestreams to artist merch, Amazon Music is innovating at some of the most exciting intersections of music and culture. We offer experiences that serve all listeners with our different tiers of service: Prime members get access to all the music in shuffle mode, and top ad-free podcasts, included with their membership; customers can upgrade to Amazon Music Unlimited for unlimited, on-demand access to 100 million songs, including millions in HD, Ultra HD, and spatial audio; and anyone can listen for free by downloading the Amazon Music app or via Alexa-enabled devices. Join us for the opportunity to influence how Amazon Music engages fans, artists, and creators on a global scale. The Data, Insights, Science and Optimization, Music Product and Tech (DISCO MPT) team is looking for a Data Scientist to join a team of scientists and engineers who analyze big data, provide analytics and insights and build models and algorithms to power Music product experiences. In this role, you will set the science vision and direction for the team and collaborate with internal stakeholders across product, science and finance to scale and advance our science offerings. You will lead large scale science solutions, prioritize across multiple stakeholders and projects and be part of a fast-paced, dynamic and fun environment. Key job responsibilities • Lead the research and development of models and science products powering personalized recommendations • Partner with product leaders at Amazon Music to develop science-driven business strategies • Partner with science, marketing and product teams across Amazon Entertainment and subscription businesses • Educate internal teams on analytics, insights and measurement • Develop models to determine drivers of key performance metrics, and automate the process of deep diving into variances • Collaborate with product and engineering teams to evaluate the impact of new features or algorithms (e.g., the Playlist Song Recommendation experiments) • Analyze the results of experiments and provide recommendations to optimize solutions • Partner with Senior Data Scientists and Product Managers to analyze and propose success and guardrail metrics • Encourage the use of experimentation and advanced analytics across the organization About the team The DISCO team focuses on accelerating Amazon Music customer growth by empowering product teams to make sound, customer-centric decisions through data and insights. We build data pipelines, self-service analytics, insights and predictive models enabling acquisition, engagement and retention at scale with personalized customer touchpoints.

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.

We are seeking an Applied Science Manager to lead a team building Amazon’s next-generation customer memory and personalization systems. Are you interested in building systems that move beyond reacting to customer behavior, to actually understanding and remembering it over time? Our team is building Amazon’s customer memory layer – a system that extracts, curates, and reasons over customer knowledge to power next-generation personalization. This includes transforming noisy, unstructured signals into durable, high-quality representations of customer preferences, intents, and life events, and using them in real time to improve customer experiences. We are part of Amazon’s Personalization organization, a high-performing group that leverages large-scale machine learning, generative AI, and distributed systems to deliver highly relevant customer experiences. We tackle challenging problems at the intersection of information extraction, knowledge representation, LLM reasoning, and recommendation systems. Our systems operate under real-world constraints of scale, latency, and quality, requiring careful tradeoffs between precision, recall, and responsiveness. This team plays a central role in defining how Amazon understands its customers, and how that understanding is applied across the shopping experience. As an Applied Science Manager, you will lead a team of scientists working on LLM-powered memory and personalization systems. You will define the scientific direction for how customer knowledge is extracted, validated, and applied in production systems. Key job responsibilities As an Applied Science Manager, you will lead a team of scientists working on LLM-powered memory and personalization systems. You will define the scientific direction for how customer knowledge is extracted, validated, and applied in production systems. You will own the end-to-end delivery of ML solutions, from problem formulation and modeling to offline and online experimentation, and production deployment at scale. You will ensure your team delivers high-quality, scalable systems that power customer-facing experiences. You will drive work across areas such as fact extraction, memory quality and lifecycle, temporal reasoning, and grounded personalization, while navigating tradeoffs between quality, latency, and coverage. You will hire and develop scientists, raise the scientific bar, and foster a culture of ownership and rigor. You will partner closely with engineering and product teams to translate research into measurable customer impact, and represent your team’s work to senior leadership. Please visit https://www.amazon.science for more information.

Amazon's 1P retail pricing systems make millions of automated decisions every day to deliver competitive prices to customers. As agentic shopping tools become more prevalent, we work to ensure these systems continue to produce efficient, customer-beneficial outcomes and avoid defects. We're hiring a Sr. Economist to help lead this effort. You'll develop the science for understanding how Amazon's pricing systems behave around our major promotional events and design mechanisms that improve long-run customer value. The role sits at the intersection of economic theory, applied econometrics, and production systems — and requires someone who can operate independently across all three. Key job responsibilities Own the economic modeling of how our pricing systems behave during our major promotional events — characterizing when our automated decisions produce efficient outcomes versus persistent defects, and designing mechanisms to correct the latter Develop econometric identification strategies for measuring the impact of pricing defects and the value of proposed corrections Deliver high-stakes analyses connecting pricing to customer trust and long-run business outcomes at VP+ level Translate economic models into system-level requirements that engineering teams can implement as automated safeguards Serve as cross-team advisor to partner teams in Demand Science, Finance, and Product About the team P2OS is a 12-person team of applied scientists and economists within WW Pricing & Promotions. We own the science behind Amazon's retail pricing decisions — optimization, demand estimation, price prediction, and error detection. The team values rigorous identification, clean problem framing, and work that changes how systems behave. You'll join a team that includes a Principal Economist, senior applied scientists, and dedicated experimentation leads — with direct access to pricing leadership.

Interested in influencing what customers around the world see when they turn on Prime Video? The Prime Video Personalization and Discovery team matches customers with the right content at the right time, at all touch points throughout the content discovery journey. We are looking for a customer-focused, solutions-oriented Sr Manager Data Science and Analytics to develop next-gen measurement and experimentation systems within Prime Video Personalization and Discovery. You'll lead an embedded science and business intelligence team driving projects across product and engineering teams that ultimately influence what millions of customers around the world see when the log into Prime Video. The ideal candidate brings experience leading experiment-based measurement and business intelligence engineering systems, excellent stakeholder communication skills, and the ability to balance technical rigor with delivery speed and customer impact. You will build cross-functional support within Prime Video for high-quality, rigorous measurement, assess business problems, and support iterative scientific solutions that balance short-term delivery with long-term science roadmaps. Key job responsibilities Key job responsibilities - Build and manage a team of Data Scientists, BI Engineers and Data Engineers - Define and drive the multi-year vision for business intelligence, experimentation and measurability in Prime Video - Partner with product stakeholders and science peers to identify strategic data-driven opportunities to improve the customer experience - Communicate findings, conclusions, and recommendations to technical and non-technical business leaders across Prime Video - Educate senior leaders about and advocate for high-quality measurement as an input to data-driven decisions - Mentor junior scientists, BI Engineers and review technical artifacts to ensure quality - Stay up-to-date on the latest data science, measurability, AI tools, techniques, and best practices and help evangelize them across the organization

At Amazon Selection and Catalog Systems (ASCS), our mission is to power the online buying experience for customers worldwide so they can find, discover, and buy any product they want. We innovate on behalf of our customers to ensure uniqueness and consistency of product identity and to infer relationships between products in Amazon Catalog to drive the selection gateway for the search and browse experiences on the website. We're solving a fundamental AI challenge: establishing product identity and relationships at unprecedented scale. Using Generative AI, Visual Language Models (VLMs), and multimodal reasoning, we determine what makes each product unique and how products relate to one another across Amazon's catalog. The scale is staggering: billions of products, petabytes of multimodal data, millions of sellers, dozens of languages, and infinite product diversity—from electronics to groceries to digital content. The research challenges are immense. GenAI and VLMs hold transformative promise for catalog understanding, but we operate where traditional methods fail: ambiguous problem spaces, incomplete and noisy data, inherent uncertainty, reasoning across both images and textual data, and explaining decisions at scale. Establishing product identities and groupings requires sophisticated models that reason across text, images, and structured data—while maintaining accuracy and trust for high-stakes business decisions affecting millions of customers daily. Amazon's Item and Relationship Platform group is looking for an innovative and customer-focused applied scientist to help us make the world's best product catalog even better. In this role, you will partner with technology and business leaders to build new state-of-the-art algorithms, models, and services to infer product-to-product relationships that matter to our customers. You will pioneer advanced GenAI solutions that power next-generation agentic shopping experiences, working in a collaborative environment where you can experiment with massive data from the world's largest product catalog, tackle problems at the frontier of AI research, rapidly implement and deploy your algorithmic ideas at scale, across millions of customers. Key job responsibilities Key job responsibilities include: * Formulate novel research problems at the intersection of GenAI, multimodal learning, and large-scale information retrieval—translating ambiguous business challenges into tractable scientific frameworks * Design and implement leading models leveraging VLMs, foundation models, and agentic architectures to solve product identity, relationship inference, and catalog understanding at billion-product scale * Pioneer explainable AI methodologies that balance model performance with scalability requirements for production systems impacting millions of daily customer decisions * Own end-to-end ML pipelines from research ideation to production deployment—processing petabytes of multimodal data with rigorous evaluation frameworks * Define research roadmaps aligned with business priorities, balancing foundational research with incremental product improvements * Mentor peer scientists and engineers on advanced ML techniques, experimental design, and scientific rigor—building organizational capability in GenAI and multimodal AI * Represent the team in the broader science community—publishing findings, delivering tech talks, and staying at the forefront of GenAI, VLM, and agentic system research

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 a Senior Applied Scientist on the team, you will be at the forefront of innovation, developing measurement solutions end-to-end from inception to production. You will set the technical vision and innovate on behalf of our customers. You will propose, design, analyze, and productionize models to provide novel measurement insights to our customers. You will partner with engineering to deploy these solutions into production. You will work with key stakeholders from various business teams to enable advertisers to act upon those metrics. Key job responsibilities * Lead the development of ad measurement models and solutions that address the full spectrum of an advertiser's investment, focusing on scalable and efficient methodologies. * Collaborate closely with cross-functional teams including engineering, product management, and business teams to define and implement measurement solutions. * Use state-of-the-art scientific technologies including Generative AI, Classical Machine Learning, Causal Inference, Natural Language Processing, and Computer Vision to develop state of the art models that measure the impact of ad spend across multiple platforms and timescales. * Drive experimentation and the continuous improvement of ML models through iterative development, testing, and optimization. * Translate complex scientific challenges into clear and impactful solutions for business stakeholders. * Mentor and guide junior scientists, fostering a collaborative and high-performing team culture. * Foster collaborations between scientists to move faster, with broader impact. * Regularly engage with the broader scientific community with presentations, publications, and patents. A day in the life You will solve real-world problems by getting and analyzing large amounts of data, generate business insights and opportunities, design simulations and experiments, and develop statistical and ML models. The team is driven by business needs, which requires collaboration with other Scientists, Engineers, and Product Managers across the advertising organization. You will prepare written and verbal presentations to share insights to audiences of varying levels of technical sophistication. Team video https://advertising.amazon.com/help/G4LNN5YWHP6SM9TJ 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.