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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 add-on subscriptions such as Apple TV+, Max, 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 and advertising? Prime Video's technology teams are creating best-in-class digital video experiences, and our Advertising Product & Technology organization is at the forefront of revolutionizing the streaming advertising landscape. The Prime Video Advertising team delivers ad tech solutions that power Prime Video's rapidly growing advertising business across video-on-demand (VOD), live streaming, and display ads—delivering value to both advertisers and viewers worldwide. We focus on critical areas including ad delivery, machine learning-driven optimization, experimentation, audience measurement, and generative AI-powered ad creative solutions. We are seeking a Senior Manager, Applied Science to lead a team of scientists and engineers building machine learning and AI solutions that directly impact Prime Video's advertising business. In this role, you will own the science strategy and execution for key workstreams including: - Ad Load Optimization – Balancing advertising revenue with viewer engagement through sophisticated ML models that determine optimal ad frequency, placement, and duration - Yield Optimization – Maximizing advertising revenue through intelligent allocation, pricing, and forecasting models - Experimentation & Metrics – Designing and scaling experimentation frameworks and causal inference methods to measure the impact of advertising decisions on both business outcomes and customer experience - Ad Creative Generation & Augmentation – Leveraging generative AI to create, personalize, and enhance ad creatives at scale As a leader of leaders, you will set the 3-5 year scientific vision for your organization, build and develop a high-performing team of senior scientists and managers, and drive large-scale ML/AI initiatives that inform strategic decisions for one of the world's largest streaming advertising platforms. You will collaborate closely with engineering, product, and business teams to translate complex scientific capabilities into measurable business impact during a period of rapid growth with a path to $10B in advertising revenue. This role offers the unique opportunity to shape the science strategy for a new and fast-growing business, working at the intersection of machine learning, generative AI, causal inference, and advertising technology at Internet scale.

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.

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.

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.

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.

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.

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.

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?

Join the next revolution in robotics at Amazon's Frontier AI & Robotics team, where you'll work alongside world-renowned AI pioneers to push the boundaries of what's possible in robotic intelligence. As a Member of Technical Staff, you'll be at the forefront of developing breakthrough foundation models that enable robots to perceive, understand, and interact with the world in unprecedented ways. You'll drive independent research initiatives in areas such as perception, manipulation, science understanding, locomotion, manipulation, sim2real transfer, multi-modal foundation models and multi-task robot learning, designing novel frameworks that bridge the gap between state-of-the-art research and real-world deployment at Amazon scale. In this role, you'll balance innovative technical exploration with practical implementation, collaborating with platform teams to ensure your models and algorithms perform robustly in dynamic real-world environments. You'll have access to Amazon's vast computational resources, enabling you to tackle ambitious problems in areas like very large multi-modal robotic foundation models and efficient, promptable model architectures that can scale across diverse robotic applications. Key job responsibilities - Drive independent research initiatives across the robotics stack, including robotics foundation models, focusing on breakthrough approaches in perception, and manipulation, for example open-vocabulary panoptic scene understanding, scaling up multi-modal LLMs, sim2real/real2sim techniques, end-to-end vision-language-action models, efficient model inference, video tokenization - Design and implement novel deep learning architectures that push the boundaries of what robots can understand and accomplish - Lead full-stack robotics projects from conceptualization through deployment, taking a system-level approach that integrates hardware considerations with algorithmic development, ensuring robust performance in production environments - Collaborate with platform and hardware teams to ensure seamless integration across the entire robotics stack, optimizing and scaling models for real-world applications - Contribute to the team's technical strategy and help shape our approach to next-generation robotics challenges A day in the life - Design and implement novel foundation model architectures and innovative systems and algorithms, leveraging our extensive infrastructure to prototype and evaluate at scale - Collaborate with our world-class research team to solve complex technical challenges - Lead technical initiatives from conception to deployment, working closely with robotics engineers to integrate your solutions into production systems - Participate in technical discussions and brainstorming sessions with team leaders and fellow scientists - Leverage our massive compute cluster and extensive robotics infrastructure to rapidly prototype and validate new ideas - Transform theoretical insights into practical solutions that can handle the complexities of real-world robotics applications 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 innovative 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 massive 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.