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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? Prime Video's technology teams are creating best-in-class digital video experience. As a Prime Video technologist, 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! Key job responsibilities - Build sequential decision-making frameworks (e.g., MDPs, multi-armed bandits, dynamic programming) to optimize marketing resource allocation over time under uncertainty. - Create predictive models to forecast marketing efficiency and aid strategic budget allocation across channels, campaigns, and markets. - Design and analyze geo-level and regional hold-out experiments to validate model predictions and establish ground truth. - Automate and scale our modeling infrastructure to improve efficiency and expand coverage across use cases and geographies. - Collaborate with leaders across business and finance teams to translate business questions into well-posed statistical and optimization problems. - Quantify uncertainty in model outputs and communicate results, limitations, and recommendations clearly to non-technical stakeholders. About the team The Marketing Science team drives decision-making on Global Prime Video marketing efforts by delivering sophisticated marketing measurement and optimization models. As an Applied Scientist on the team, you will build optimization and forecasting systems that leverage marketing effectiveness insights to deliver actionable investment recommendations. This includes sequential decision-making frameworks that adapt marketing strategy over time as customer behavior evolves. You will work closely with business and finance stakeholders, as well as other members of our science team, to shape and deliver a roadmap of models. Your frameworks will inform critical decisions for the business.

Employer: Amazon Development Center U.S., Inc. Position: Applied Scientist III - AMZ27579.1 Location: New York, NY Multiple Positions Available: Participate in the design, development, evaluation, deployment and updating of data-driven models and analytical solutions for machine learning (ML) and/or natural language (NL) applications. Develop and/or apply statistical modeling techniques (e.g. Bayesian models and deep neural networks), optimization methods, and other ML techniques to different applications in business and engineering. Routinely build and deploy ML models on available data, and run and analyze experiments in a production environment. Identify new opportunities for research in order to meet business goals. Research and implement novel ML and statistical approaches to add value to the business. Mentor junior engineers and scientists. Telecommuting may be permitted. (40 hours / week, 8:00am-5:00pm, Salary Range $183800 - $248700) Amazon.com is an Equal Opportunity – Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation

The Models, Quantum, and Silicon (MQS) Center for Quantum Computing (CQC) is a multi-disciplinary team of scientists, engineers, and technicians, on a mission to develop a fault-tolerant quantum computer. We are looking to hire a Research Software Engineer to join our growing Software team. You will work closely with our experimental physics teams to enable their work characterizing, calibrating, and operating novel quantum devices. The ideal candidate should be able to translate high-level science requirements into software implementations (e.g. Python APIs/frameworks, data analysis pipelines, calibration nodes) that are performant, scalable, and intuitive. This requires someone who (1) has a strong desire to work within a team of scientists and engineers, and (2) demonstrates ownership in initiating and driving projects to completion. This role has a particular emphasis on working directly with experimental physicists to develop scientific software workflows that enable scaling to larger quantum devices. Inclusive Team Culture Here at Amazon, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon conferences, inspire us to never stop embracing our uniqueness. Diverse Experiences Amazon 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. Mentorship & Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud. Export Control Requirement Due to applicable export control laws and regulations, candidates must be either a U.S. citizen or national, U.S. permanent resident (i.e., current Green Card holder), or lawfully admitted into the U.S. as a refugee or granted asylum, or be able to obtain a US export license. If you are unsure if you meet these requirements, please apply and Amazon will review your application for eligibility. Key job responsibilities - Architect extensible & intuitive frameworks for running quantum computing experiments and analyzing data. - Leverage the latest techniques in quantum calibration to enable scaling to larger devices. - Optimize the performance of experiment & analysis tools to enable faster experiment throughput. - Develop dashboards that allow experimentalists to inspect and control the state of quantum device calibration. - Deploy and maintain cloud infrastructure that supports increasingly-complex science workflows. - Empower scientists to actively contribute to the codebase through mentorship and documentation. We are looking for candidates with strong engineering principles, a bias for action, superior problem-solving, and excellent communication skills. Working effectively within a team environment is essential. As a Research Software Engineer embedded in a broader research science organization, you will have the opportunity to work on new ideas and stay abreast of the field of experimental quantum computation. A day in the life The majority of your time will be spent on projects that extend the functional capabilities or performance of our internal research software stack. This requires working backwards from the needs of our science staff in the context of our larger experimental roadmap. You will translate science and software requirements into design proposals balancing implementation complexity against time-to-delivery. Once a design proposal has been reviewed and accepted, you’ll drive implementation and coordinate with internal stakeholders to ensure a smooth roll out. Because many high-level experimental goals have cross-cutting requirements, you’ll often work closely with other engineers or scientists or on the team. About the team You will be joining the Software group within the MQS Center of Quantum Computing. Our team is comprised of scientists and software engineers who are building scalable software that enables quantum computing technologies.

Amazon Strategic Account Services (SAS) Tech Organization is looking for an Applied Scientist Applied Scientist who can autonomously drive scientific innovations from research to production, developing sophisticated AI solutions that serve both Amazon's global seller base and internal Marketplace Consultants. Working in a highly collaborative environment, you'll leverage expertise in machine learning, operations research, and statistics to translate theoretical advances in LLMs, probabilistic modeling, and optimization into practical applications. The role demands strong capabilities in prototyping and iterative improvement, bridging cutting models with real-world applications while maintaining scientific rigor and measurable business impact. Key job responsibilities - Lead the development of sophisticated AI solutions leveraging deep learning, LLMs, and advanced machine learning techniques to transform both seller operations and internal consultancy capabilities at scale - Define and drive long-term scientific vision for the organization, translating complex business challenges into innovative technical solutions that advance the state-of-the-art in applied machine learning - Design and implement advanced ML architectures combining multiple learning paradigms - from reinforcement learning and causal inference to predictive modeling - to tackle critical marketplace challenges - Architect next-generation recommendation and optimization systems that handle complex multi-dimensional constraints while maintaining robustness and interpretability at scale - Drive end-to-end development of AI applications from research through production, collaborating with engineering teams to ensure successful deployment and conducting rigorous A/B experiments to validate impact - Pioneer novel applications of foundation models and generative AI, developing sophisticated evaluation frameworks while maintaining Amazon's high standards for accuracy and reliability - Lead technical discussions across organizational boundaries, effectively communicating complex scientific concepts to diverse stakeholders while staying at the forefront of ML/AI research advancements About the team What is Amazon Strategic Account Services (SAS)? The SAS team aims to accelerate the full potential of our Sellers, helping them to navigate the increasing complexity of the e-commerce space. Our team provides in-depth strategic consultancy using a data-driven, collaborative, and a Customer-focused approach to achieve commercial goals of Amazon Sellers.

Amazon's Middle Mile Surface Research Science seeks an Applied Scientist to invent and build optimization models and algorithm to improve how Amazon plans and operates its transportation network. Amazon's transportation network moves millions of truckloads of freight between vendors, warehouses, and customers using a fleet of trucks, trains, and airplanes, on time and at low cost. Operating it requires constant decisions about how to route, schedule, and balance capacity across the network, and our strategy is to make those decisions with science-driven technology. Because existing techniques rarely fit Amazon's scale and unique business needs, this role centers on inventing new approaches and algorithms. As an Applied Scientist, you'll develop optimization models and algorithms. Your role will initially focus on driver capacity optimization. Your models will impact business decisions worth billions of dollars and improve the delivery experience for millions of customers. Key job responsibilities - Design and develop optimization models and algorithms that enhance our optimization and planning systems. - Build models and algorithms from prototype to production-level systems. - Translate ambiguous business problems into modeling approaches, and drive the technical design with product, engineering, and operations partners. - Influence key business decisions through rigorous modeling and analysis. - Communicate results and recommendations to scientific and business audiences. A day in the life - Analyze data to investigate a business problem or model performance and identify improvements - Brainstorm new algorithmic strategies or business opportunities with fellow scientists - Leverage GenAI to build and test your new model features - Run a simulation or experiment to evaluate your model’s performance - Meet with product and tech partners to review project requirements, data, design, or other project decisions - Review code changes or a design document from a fellow scientist or engineers - Write and present a paper documenting algorithm features, results, and recommendations About the team Middle Mile Surface Research Science builds the models and algorithms that plan and operate Amazon's middle mile network. Our work spans operations research, optimization, and machine learning. We work on vehicle route planning, capacity planning, scheduling, network design, transit-time prediction, demand forecasting, and equipment re-balancing. Our team of about ten scientists is part of a broader science organization whose scientists bring deep expertise in machine learning and optimization. We optimize decisions worth billions of dollars and reach millions of customers.

Amazon's Middle Mile Surface Research Science seeks an Applied Scientist to invent and build machine learning models that improve how Amazon plans and operates its transportation network. Amazon's transportation network moves millions of truckloads of freight between vendors, warehouses, and customers using a fleet of trucks, trains, and airplanes, on time and at low cost. Operating it requires constant decisions about how to route, schedule, and balance capacity across the network, and our strategy is to make those decisions with science-driven technology. Because existing techniques rarely fit Amazon's scale and unique business needs, this role centers on inventing new approaches and algorithms. As an Applied Scientist, you'll develop machine learning, forecasting, and prediction models and algorithms. Your first project will focus on trailer imbalance forecasting and safety stock optimization to improve our equipment re-balancing strategy, where you'll own the prediction models and grow your scope over time. Your models will impact business decisions worth billions of dollars and improve the delivery experience for millions of customers. Key job responsibilities - Design and develop machine learning, forecasting, and other prediction models that enhance our optimization and planning systems. - Build models and algorithms from prototype to production-level systems. - Translate ambiguous business problems into modeling approaches, and drive the technical design with product, engineering, and operations partners. - Influence key business decisions through rigorous modeling and analysis. - Communicate results and recommendations to scientific and business audiences. A day in the life - Analyze data to investigate a business problem or model performance and identify improvements - Brainstorm new algorithmic strategies or business opportunities with fellow scientists - Leverage GenAI to build and test your new model features - Run a simulation or experiment to evaluate your model’s performance - Meet with product and tech partners to review project requirements, data, design, or other project decisions - Review code changes or a design document from a fellow scientist or engineers - Write and present a paper documenting algorithm features, results, and recommendations About the team Middle Mile Surface Research Science builds the models and algorithms that plan and operate Amazon's middle mile network. Our work spans operations research, optimization, and machine learning. We work on vehicle route planning, capacity planning, scheduling, network design, transit-time prediction, demand forecasting, and equipment re-balancing. Our team of about ten scientists is part of a broader science organization whose scientists bring deep expertise in machine learning and optimization. We optimize decisions worth billions of dollars and reach millions of customers.

Amazon's Middle Mile Surface Research Science seeks an Applied Scientist to invent and build machine learning models that improve how Amazon plans and operates its transportation network. Amazon's transportation network moves millions of truckloads of freight between vendors, warehouses, and customers using a fleet of trucks, trains, and airplanes, on time and at low cost. Operating it requires constant decisions about how to route, schedule, and balance capacity across the network, and our strategy is to make those decisions with science-driven technology. Because existing techniques rarely fit Amazon's scale and unique business needs, this role centers on inventing new approaches and algorithms. As an Applied Scientist, you'll develop machine learning, forecasting, and prediction models and algorithms. You role will initially develop transit time prediction and uncertainty models. Your models will impact business decisions worth billions of dollars and improve the delivery experience for millions of customers. Key job responsibilities - Design and develop machine learning, forecasting, and other prediction models that enhance our optimization and planning systems. - Build models and algorithms from prototype to production-level systems. - Translate ambiguous business problems into modeling approaches, and drive the technical design with product, engineering, and operations partners. - Influence key business decisions through rigorous modeling and analysis. - Communicate results and recommendations to scientific and business audiences. A day in the life - Analyze data to investigate a business problem or model performance and identify improvements - Brainstorm new algorithmic strategies or business opportunities with fellow scientists - Leverage GenAI to build and test your new model features - Run a simulation or experiment to evaluate your model’s performance - Meet with product and tech partners to review project requirements, data, design, or other project decisions - Review code changes or a design document from a fellow scientist or engineers - Write and present a paper documenting algorithm features, results, and recommendations About the team Middle Mile Surface Research Science builds the models and algorithms that plan and operate Amazon's middle mile network. Our work spans operations research, optimization, and machine learning. We work on vehicle route planning, capacity planning, scheduling, network design, transit-time prediction, demand forecasting, and equipment re-balancing. Our team of about ten scientists is part of a broader science organization whose scientists bring deep expertise in machine learning and optimization. We optimize decisions worth billions of dollars and reach millions of customers.

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 M&G) 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 marketing tech, 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 customer identity, acquisition, and retention strategies for Amazon Music • Partner with Marketing and Growth leadership to develop science-driven business strategies, including subscriber segmentation, lifecycle management, and upsell programs • Build predictive models — including propensity, uplift, and causal inference models — to support targeted marketing initiatives, measure incremental impact, and automate deep dives into key metric variances • Collaborate with product, engineering, and marketing teams to evaluate the impact of growth campaigns, features, and algorithmic interventions through rigorous experimentation • Analyze experiment results and provide data-driven recommendations to optimize subscriber acquisition and retention outcomes • Partner with Senior Data Scientists and Product Managers to define, propose, and validate success and guardrail metrics for identity and growth programs • Educate marketing and growth teams on analytics, measurement frameworks, and experimentation best practices, encouraging a culture of evidence-based decision making 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 touch points.

Amazon's Price Perception and Evaluation team is seeking a driven Applied Scientist to harness planet scale multi-modal datasets, and navigate a continuously evolving competitor landscape, in order to build and scale an advanced self-learning scientific price estimation and product understanding system, regularly generating fresh customer-relevant prices on billions of Amazon and Third Party Seller products worldwide. The Applied Scientist will work closely with other research scientists, machine learning experts, and economists to design and run experiments, research new algorithms, and find new ways to improve Seller Pricing to optimize the Customer experience. The Scientist will partner with technology and product leaders to solve business and technology problems using scientific approaches to build new services that surprise and delight our customers. Key job responsibilities - Research and use of statistical techniques to create scalable solutions for business problems. - Design, build, and deploy effective and innovative ML solutions to provide low prices and increased selection for customers using scientifically-based methods and decision making. - Evaluate the proposed solutions via offline benchmark tests as well as online A/B tests in production. - Establish scalable, efficient, automated processes for large scale data analyses, model development, validation and implementation. - Publish and present your work at internal and external scientific venues.

Amazon Advertising is one of Amazon's fastest growing and most profitable businesses. As a core product offering within our advertising portfolio, Sponsored Products (SP) helps merchants, retail vendors, and brand owners succeed via native advertising, which grows incremental sales of their products sold through Amazon. The SP team's primary goals are to help shoppers discover new products they love, be the most efficient way for advertisers to meet their business objectives, and build a sustainable business that continuously innovates on behalf of customers. Our products and solutions are strategically important to enable our Retail and Marketplace businesses to drive long-term growth. We deliver billions of ad impressions and millions of clicks and break fresh ground in product and technical innovations every day! Within Sponsored Products, the Bidding team is responsible for defining and delivering a collection of advertising products around bid controls (dynamic bidding, bid recommendations, etc.) that drive discovery and sales. Our solutions generate billions in revenue and drive long-term growth for Amazon’s Retail and Marketplace businesses. We deliver billions of ad impressions, millions of clicks daily, and break fresh ground to create world-class products. We are highly motivated, collaborative, and fun-loving team with an entrepreneurial spirit - with a broad mandate to experiment and innovate. You will invent new experiences and influence customer-facing shopping experiences to help suppliers grow their retail business and the auction dynamics that leverage native advertising; this is your opportunity to work within the fastest-growing businesses across all of Amazon! Define a long-term science vision for our advertising business, driven fundamentally from our customers' needs, translating that direction into specific plans for research and applied scientists, as well as engineering and product teams. This role combines science leadership, organizational ability, technical strength, product focus, and business understanding. Key job responsibilities As a Senior Applied Scientist on this team, you will: • Lead a new initiative across Sponsored Products Bidding focused on AI/ML based features. • Be the technical leader in AI, Machine Learning; lead efforts within this team and across other teams. • Perform hands-on analysis and modeling of enormous data sets to develop insights that increase traffic monetization and merchandise sales, without compromising the shopper experience. • Drive end-to-end AI/Machine Learning projects that have a high degree of ambiguity, scale, complexity. • Build models, perform proof-of-concept, experiment, optimize, and deploy your models into production; work closely with software engineers to assist in productionizing your AI/ML models. • Run A/B experiments, gather data, and perform statistical analysis. • Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving. • Research new and innovative AI/ machine learning approaches. • Recruit Applied Scientists to the team and provide mentorship. A day in the life Why you will love this opportunity: Amazon is investing heavily in building a world-class advertising business. This team defines and delivers a collection of advertising products that drive discovery and sales. Our solutions generate billions in revenue and drive long-term growth for Amazon's Retail and Marketplace businesses. We deliver billions of ad impressions, millions of clicks daily, and break fresh ground to create world-class products. We are a highly motivated, collaborative, and fun-loving team with an entrepreneurial spirit - with a broad mandate to experiment and innovate. Impact and Career Growth: You will invent new experiences and influence customer-facing shopping experiences to help suppliers grow their retail business and the auction dynamics that leverage native advertising; this is your opportunity to work within the fastest-growing businesses across all of Amazon! Define a long-term science vision for our advertising business, driven from our customers' needs, translating that direction into specific plans for research and applied scientists, as well as engineering and product teams. This role combines science leadership, organizational ability, technical strength, product focus, and business understanding. About the team The Sponsored Products and Brands team at Amazon Ads is re-imagining the advertising landscape through the latest generative AI technologies, revolutionizing how millions of customers discover products and engage with brands across Amazon.com and beyond. We are at the forefront of re-inventing advertising experiences, bridging human creativity with artificial intelligence to transform every aspect of the advertising lifecycle from ad creation and optimization to performance analysis and customer insights. We are a passionate group of innovators dedicated to developing responsible and intelligent AI technologies that balance the needs of advertisers, enhance the shopping experience, and strengthen the marketplace. If you're energized by solving complex challenges and pushing the boundaries of what's possible with AI, join us in shaping the future of advertising. The SPB Bidding team within Sponsored Products and Brands is focused on guiding and supporting Millions of advertisers to meet their advertising needs of creating and managing ad campaigns. At this scale, the complexity of diverse advertiser goals, campaign types, and market dynamics creates both a massive technical challenge and a transformative opportunity: even small improvements in bidding systems can have outsized impact on advertiser success and Amazon’s retail ecosystem. Our vision is to build a highly personalized, context-aware bidding system that leverages auction simulations, ML models, and optimization algorithms. This framework, will operate across SPB bidding system and proactively delivering value based on deep understanding of the advertiser. To execute this vision, we collaborate closely with stakeholders across Ad Console, Sales, and Marketing to identify opportunities—from high-level product guidance down to granular recommendations—and deliver them through a tailored, personalized experience. Our work is grounded in state-of-the-art bidding agent architectures, tool integration, reasoning frameworks, and model customization approaches (including tuning and preference optimization), ensuring our systems are both scalable and adaptive.