Careers

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.

AWS Insights & Optimization is looking for an Applied Scientist to help develop sophisticated algorithms and models that involve analyzing and learning from over 540 billion customer cost, usage, and utilization events daily. We use this data to power cost anomaly detection, cost forecasting, rightsizing recommendations across seven compute services, Savings Plans optimization, and AI-powered conversational experiences that help customers understand and optimize their AWS spend. Our team's vision is to be the world's provider of intelligent AWS cloud financial management, where customers can understand, control, and optimize usage of AWS products. We sit at the nexus of all AWS services and interact directly with end-customers, building relationships with teams across AWS to ensure we offer a secure and reliable experience that builds trust and provides intelligent insights. As a successful Applied Scientist in AWS Insights & Optimization, you will own models end-to-end — from problem formulation through experimentation to production deployment. Your work may span cost anomaly detection (decomposition, detection, and root cause attribution), time-series forecasting with a focus on accuracy and consistency, rightsizing engines for EC2, EBS, Lambda, ECS, RDS, and Aurora, LLM evaluation science for AI-powered agent experiences, or agent memory architectures that enable persistent, adaptive behavior across sessions. You will work closely with applied scientists, software engineers, and product teams to enhance existing models and build new ones that solve challenging customer problems. You will drive implementation of proposed models, establish testing strategies to validate them before and after production, and define evaluation metrics that determine whether capabilities meet the quality bar. We value accuracy over speed, measurability over intuition, and simplicity over complexity — the simplest model that meets the bar wins. You are an analytical problem solver who enjoys diving into data, are excited about investigating and developing algorithms, and can influence technical teams and business stakeholders to solve real-world customer problems.

Prime Video is a first-stop entertainment destination offering customers a vast collection of premium programming in one app available across thousands of devices. Prime members can customize their viewing experience and find their favorite movies, series, documentaries, and live sports – including Amazon MGM Studios-produced series and movies; licensed fan favorites; and programming from Prime Video subscriptions such as Apple TV+, HBO Max, Peacock, Crunchyroll and MGM+. All customers, regardless of whether they have a Prime membership or not, can rent or buy titles via the Prime Video Store, and can enjoy even more content for free with ads. Are you interested in shaping the future of entertainment? Prime Video's technology teams are creating best-in-class digital video experience. As a Prime Video team member, you’ll have end-to-end ownership of the product, user experience, design, and technology required to deliver state-of-the-art experiences for our customers. You’ll get to work on projects that are fast-paced, challenging, and varied. You’ll also be able to experiment with new possibilities, take risks, and collaborate with remarkable people. We’ll look for you to bring your diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. With global opportunities for talented technologists, you can decide where a career Prime Video Tech takes you! Are you passionate about building intelligent systems that personalize experiences for hundreds of millions of customers? Do you want to work at the intersection of deep learning, causal inference, and product optimization, where your models directly shape how customers experience advertising on one of the world’s largest streaming platforms? Prime Video Ads is redefining what ad-supported streaming looks like. We launched ads on Prime Video in 2024 and are now in the process of building the science foundation to make our ad experience the most customer-centric in the industry. Unlike traditional digital advertising where clicks and conversions provide direct feedback, streaming presents unique scientific challenges. Indirect signals, heterogeneous customer responses, and the need to balance monetization with long-term engagement, all at massive scale. Key job responsibilities The Science Our team tackles problems that span the full ML lifecycle, from exploratory research and offline modeling to online experimentation and production deployment. The science challenges include: * Heterogeneous customer responses. The same customer responds differently to ads depending on what they’re watching, how engaged they are, and their broader streaming context. We need to understand and predict this variation to make better advertising decisions in real time. * Signal sparsity in streaming. Unlike search or retail advertising, streaming offers no clicks, no conversions, and limited direct feedback. We must develop creative approaches to infer customer preferences, intent, and tolerance from indirect behavioral signals. * Personalization at scale. A one-size-fits-all ad experience leaves value on the table for both customers and the business. We build systems that adapt ad load, placement, and content to individual viewers across 100M+ customers and 100k+ titles. * Small effects, large variance. Ad interventions typically produce 0.1-2% shifts in engagement, effects easily overwhelmed by natural behavioral variance. Measuring, attributing, and optimizing these small signals requires rigorous experimental design and causal methodologies. * Competing objectives. Revenue, customer engagement, long-term retention, and advertiser value are in tension. We develop principled frameworks to navigate these tradeoffs and optimize for sustainable outcomes rather than any single metric. A day in the life * Lead the research and development of ML models that personalize advertising decisions for 100M+ customers across 100k+ titles, with production deployment in mind * Develop deep learning architectures (multi-task learning, embedding-based representations) for customer behavior prediction at scale * Design and analyze large-scale A/B experiments, applying causal inference techniques to measure and optimize the impact of ad strategies on customer engagement and monetization Partner with engineering to ensure models meet production latency and scalability requirements * Collaborate with product managers to frame business problems as tractable ML problems and translate findings into product decisions * Shape the team’s scientific roadmap, identifying high-impact research directions About the team The PV Ad CX team’s mission is to create the world’s most customer-centric ad experience for video streaming. We build adaptive systems that determine when, how many, and what ads to show each customer, personalized to their viewing behavior, content context, and engagement patterns. We aspire to transform ad breaks from interruptions into moments that feel relevant and thoughtful.

Join us in the evolution of Amazon’s Seller business! The Selling Partner Growth organization is the growth and development engine for our Store. Partnering with business, product, and engineering, we catalyze SP growth with comprehensive and accurate data, unique insights, and actionable recommendations and collaborate with WW SP facing teams to drive adoption and create feedback loops. We strongly believe that any motivated SP should be able to grow their businesses and reach their full potential supported by Amazon tools and resources. We are looking for a Applied Scientist II to work on our growth agent vision on seller recommendation to improve our SP growth strategy and drive new seller success. As a successful Applied Scientist on our talented team of applied scientists and economists, you will leverage the latest GenAI technology to solve complex problems, and collaborate with engineering, research, and business teams to deliver agentic experience on behalf of sour sellers. You need to have deep understanding on the business domain and have the ability to connect business with science. You are also strong in GenAI technology and scientific foundation with the ability to collaborate with engineering to put models in production to answer specific business questions. You are an expert at synthesizing and communicating insights and recommendations to audiences of varying levels of technical sophistication. You will continue to contribute to the research community, by working with scientists across Amazon, as well as collaborating with academic researchers and publishing papers (www.aboutamazon.com/research). Key job responsibilities As an Applied Scientist II in the team, you will: - Identify opportunities to improve SP growth and translate those opportunities into science problems via principled GenAI solutions . - Design and execute roadmaps for complex science projects to help SP have a delightful selling experience while creating long term value for our shoppers. - Work with our engineering partners and draw upon your experience to meet latency and other system constraints. - Be responsible for communicating our science innovations to the broader internal & external scientific community.

Excited by the disruptive potential of quantum technology? Want to innovate on behalf of our customers to build quantum computing tools for the cloud? Thrilled to be key part of Amazon, who has been investing in disruptive innovation for decades, pioneering and shaping the world’s technology? Amazon Braket is looking for Applied Scientists in quantum computing to join an exceptional team of researchers and engineers. Quantum computing is rapidly emerging from the realms of science-fiction, and our customers can the see the potential it has to address their challenges. One of our missions at AWS is to give customers access to the most innovative technology available and help them continuously reinvent their business. Quantum computing is a technology that holds promise to be transformational in many industries, and with Amazon Braket we are adding quantum computing resources to the toolkits of every researcher and developer. As the technical lead for fault-tolerant quantum compilation, you'll own the full stack that transforms high-level quantum programs into optimized instructions that run on real hardware. You will play a key role in shaping the development roadmap of the service, and evangelizing new features and capabilities. Most importantly, you will work closely with our quantum computing research teams, as well as industry and academic partners. Our team collaborates across the entire AWS organization to get drive innovation and deliver the right solutions to our customers. A successful candidate will be a person who enjoys diving deep into customer problems, conducting independent research and development, working across teams with academic and industry experts, shaping the long-term QC strategy for AWS, and deliver the tools and systems that make useful quantum computing a reality for our customers. It will be a person who likes to have fun, loves to learn, and wants to innovate in the world of QC. Key job responsibilities - Own the technical vision and roadmap for the quantum compilation toolchain - Lead and grow a team of applied scientists and software engineers; set the bar for scientific rigor and engineering quality - Design compilation passes that minimize resource overhead for fault-tolerant quantum architectures - Collaborate with hardware, algorithms, and applications teams to co-design the compiler around real device constraints and customer workloads - Publish research and open-source contributions that advance the state of the art and attract world-class talent About the team Amazon Braket is the quantum computing service by AWS. We give researchers, developer, and enterprises access to quantum hardware and the tools to build on it. Our goal is to make quantum computing a core part of the AWS accelerated compute portfolio. The compilation team sits at the center of Braket's technical stack — bridging the gap between what customers write and what future fault-tolerant hardware executes. You'll work alongside hardware teams, scientists designing error correction protocols, and engineers building the service that bring these capabilities to our customers.

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.

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 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.

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?