Careers

Do you want to be part of a team that's revolutionizing Amazon's fulfillment and packaging technology? Are you ready to optimize systems that process tens-of-millions of customer packages daily with the lowest cost to serve and a defect-free customer experience? Do you have a passion for solving complex science challenges and building a sustainable e-commerce experience? The Robotics Delivery and Packaging Innovation (RDPI) team is seeking an Applied Scientist who will join a team of experts in the field of Machine Learning (ML), Statistics, Operations Research, Computer Vision and Generative AI to work together to break new ground in the world of automated packaging solutions. The RDPI team owns mission-critical automation and packaging solutions that impact billions of customer shipments annually across Amazon’s WW marketplaces. We manage billions of dollars in material spend and packaging labor costs while driving significant reductions in carbon emissions. Our team is revolutionizing e-commerce through advanced packaging automation, innovative sortation technology, and sustainable solutions. We're dramatically reducing single-use plastics across our network while developing next-generation automated solutions that can handle the majority of our packaging needs. We're also transforming our supply chain through strategic investments in paper manufacturing and innovative materials, driving both substantial cost savings and environmental benefits. This is an exciting opportunity to work on large-scale automation challenges that directly impact customer experience, operational efficiency, and environmental sustainability at one of the world's largest e-commerce companies. You'll work in a collaborative environment where you can pursue ambitious research with many peta-bytes of data, work on problems that haven’t been solved before, quickly implement and deploy your algorithmic ideas at scale, understand whether they succeed via statistically relevant experiments across millions of customers, and publish your research. You'll see the work you do directly improve the packaging experience of Amazon customers in the fulfillment technology space. If you are interested in robotics, computer vision, machine learning, operations research, statistics, big data, and building scalable solutions, this role is for you. Key job responsibilities A successful candidate in this role may perform some or all of the following responsibilities: - Leverage generative AI technologies to develop scalable solutions for automated product compatibility and safety assessments (e.g., evaluating product shipping compatibility, safety requirements, and packaging configurations) - Develop advanced AI models by extracting predictive features from multiple data sources (product/packaging images, product descriptions, sensor data, geospatial data) to forecast package-related damages and optimize packaging decisions based on customer preference prediction - Develop and implement computer vision solutions to automate packaging workflows and detect product/packaging defects in real-time operations - Build causal inference model to capture the downstream impacts of different packaging designs and delivery experience - Design and implement robotic control algorithms to optimize machine efficiency and meet diverse business objectives A day in the life Scientists on our team work daily with dedicated product and engineering partners to bring innovative solutions from concept to production. You'll divide your time between deep technical work—building models, analyzing results, iterating on algorithms—and collaborative activities like design reviews, stakeholder presentations, and cross-functional planning. You'll also have opportunities to support science initiatives across the broader RDPI organization (1500+ people), partnering with diverse teams to solve high-impact problems and scale your solutions across Amazon. About the team We are a team of scientists with diverse technical backgrounds spanning Machine Learning, Operations Research, Causal Inference, and Econometrics. We tackle complex, high-impact problems that directly influence Amazon's strategic decisions and financial performance. Our solutions typically require combining multiple methodologies, and you'll work collaboratively with other scientists while partnering closely with product and engineering teams to bring your innovations into production systems. You'll have the opportunity to grow your expertise across disciplines while delivering measurable business impact at scale.

Do you want to be part of a team that's revolutionizing Amazon's fulfillment and packaging technology? Are you ready to optimize systems that process tens-of-millions of customer packages daily with the lowest cost to serve and a defect-free customer experience? Do you have a passion for solving complex science challenges and building a sustainable e-commerce experience? The Robotics Delivery and Packaging Innovation (RDPI) team is seeking an Applied Scientist who will join a team of experts in the field of Machine Learning (ML), Statistics, Operations Research, Computer Vision and Generative AI to work together to break new ground in the world of automated packaging solutions. The RDPI team owns mission-critical automation and packaging solutions that impact billions of customer shipments annually across Amazon’s WW marketplaces. We manage billions of dollars in material spend and packaging labor costs while driving significant reductions in carbon emissions. Our team is revolutionizing e-commerce through advanced packaging automation, innovative sortation technology, and sustainable solutions. We're dramatically reducing single-use plastics across our network while developing next-generation automated solutions that can handle the majority of our packaging needs. We're also transforming our supply chain through strategic investments in paper manufacturing and innovative materials, driving both substantial cost savings and environmental benefits. This is an exciting opportunity to work on large-scale automation challenges that directly impact customer experience, operational efficiency, and environmental sustainability at one of the world's largest e-commerce companies. You'll work in a collaborative environment where you can pursue ambitious research with many peta-bytes of data, work on problems that haven’t been solved before, quickly implement and deploy your algorithmic ideas at scale, understand whether they succeed via statistically relevant experiments across millions of customers, and publish your research. You'll see the work you do directly improve the packaging experience of Amazon customers in the fulfillment technology space. If you are interested in robotics, computer vision, machine learning, operations research, statistics, big data, and building scalable solutions, this role is for you. Key job responsibilities A successful candidate in this role may perform some or all of the following responsibilities: - Leverage generative AI technologies to develop scalable solutions for automated product compatibility and safety assessments (e.g., evaluating product shipping compatibility, safety requirements, and packaging configurations) - Develop advanced AI models by extracting predictive features from multiple data sources (product/packaging images, product descriptions, sensor data, geospatial data) to forecast package-related damages and optimize packaging decisions based on customer preference prediction - Develop and implement computer vision solutions to automate packaging workflows and detect product/packaging defects in real-time operations - Build causal inference model to capture the downstream impacts of different packaging designs and delivery experience - Design and implement robotic control algorithms to optimize machine efficiency and meet diverse business objectives A day in the life Scientists on our team work daily with dedicated product and engineering partners to bring innovative solutions from concept to production. You'll divide your time between deep technical work—building models, analyzing results, iterating on algorithms—and collaborative activities like design reviews, stakeholder presentations, and cross-functional planning. You'll also have opportunities to support science initiatives across the broader RDPI organization (1500+ people), partnering with diverse teams to solve high-impact problems and scale your solutions across Amazon. About the team We are a team of scientists with diverse technical backgrounds spanning Machine Learning, Operations Research, Causal Inference, and Econometrics. We tackle complex, high-impact problems that directly influence Amazon's strategic decisions and financial performance. Our solutions typically require combining multiple methodologies, and you'll work collaboratively with other scientists while partnering closely with product and engineering teams to bring your innovations into production systems. You'll have the opportunity to grow your expertise across disciplines while delivering measurable business impact at scale.

Customer Experience and Business Trends (CXBT) is looking for an Applied Scientist to join their team. CXBT's mission is to create best-in-class AI agents that seamlessly integrate multimodal inputs like speech, images, and video, enabling natural, empathetic, and adaptive interactions. We leverage advanced architectures, cross-modal learning, interpretability, and responsible AI techniques to provide coherent, context-aware responses augmented by real-time knowledge retrieval. The ideal candidate will have expertise in Large Language Models (LLMs), speech, audio, Natural Language processing (NLP) or multimodal learning to pioneer innovations in data simulation, representation, generation, reasoning, retrieval, and evaluation. Key job responsibilities - Build scalable solutions for real-time conversational experiences, including multilingual support, customizable personalities, and conversational turn-taking. - Develop data simulation approaches that mimic real-world speech interactions. - Research and implement novel algorithms and modeling techniques. - Acquire and curate diverse datasets while ensuring user privacy. - Create robust evaluation metrics and test sets to assess language model performance. - Integrate human feedback to improve data selection and model performance. - Innovate in data representation and model training techniques. - Apply responsible AI practices throughout the development process. A day in the life Our team is dedicated to improving Amazon's products and services through evaluation of the end-to-end customer experience using both internal and external processes and technology. Our mission is to deeply understand our customers' experiences, challenge the status quo, and provide insights that drive innovation to improve that experience. Through our analysis and insights, we inform business decisions that directly impact customer experience as customers of new GenAI and LLM technologies. About the team Customer Experience and Business Trends (CXBT) is an organization made up of a diverse suite of functions dedicated to deeply understanding and improving customer experience, globally. We are a team of builders that develop products, services, ideas, and various ways of leveraging data to influence product and service offerings – for almost every business at Amazon – for every customer (e.g., consumers, developers, sellers/brands, employees, investors, streamers, gamers). Our approach is based on determining the customer need, along with problem solving, and we work backwards from there. We use technical and non-technical approaches and stay aware of industry and business trends. We are a global team, made up of a diverse set of profiles, skills, and backgrounds – including: Product Managers, Software Developers, Computer Vision experts, Solutions Architects, Data Scientists, Business Intelligence Engineers, Business Analysts, Risk Managers, and more.

Customer Experience and Business Trends (CXBT) is looking for an Applied Scientist to join their team. CXBT's mission is to create best-in-class AI agents that seamlessly integrate multimodal inputs like speech, images, and video, enabling natural, empathetic, and adaptive interactions. We leverage advanced architectures, cross-modal learning, interpretability, and responsible AI techniques to provide coherent, context-aware responses augmented by real-time knowledge retrieval. The ideal candidate will have expertise in Large Language Models (LLMs), speech, audio, Natural Language processing (NLP) or multimodal learning to pioneer innovations in data simulation, representation, generation, reasoning, retrieval, and evaluation. Key job responsibilities - Build scalable solutions for real-time conversational experiences, including multilingual support, customizable personalities, and conversational turn-taking. - Develop data simulation approaches that mimic real-world speech interactions. - Research and implement novel algorithms and modeling techniques. - Acquire and curate diverse datasets while ensuring user privacy. - Create robust evaluation metrics and test sets to assess language model performance. - Integrate human feedback to improve data selection and model performance. - Innovate in data representation and model training techniques. - Apply responsible AI practices throughout the development process. A day in the life Our team is dedicated to improving Amazon's products and services through evaluation of the end-to-end customer experience using both internal and external processes and technology. Our mission is to deeply understand our customers' experiences, challenge the status quo, and provide insights that drive innovation to improve that experience. Through our analysis and insights, we inform business decisions that directly impact customer experience as customers of new GenAI and LLM technologies. About the team Customer Experience and Business Trends (CXBT) is an organization made up of a diverse suite of functions dedicated to deeply understanding and improving customer experience, globally. We are a team of builders that develop products, services, ideas, and various ways of leveraging data to influence product and service offerings – for almost every business at Amazon – for every customer (e.g., consumers, developers, sellers/brands, employees, investors, streamers, gamers). Our approach is based on determining the customer need, along with problem solving, and we work backwards from there. We use technical and non-technical approaches and stay aware of industry and business trends. We are a global team, made up of a diverse set of profiles, skills, and backgrounds – including: Product Managers, Software Developers, Computer Vision experts, Solutions Architects, Data Scientists, Business Intelligence Engineers, Business Analysts, Risk Managers, and more.

Sponsored Products and Brands (SPB) is at the heart of Amazon Advertising, helping millions of advertisers—from small businesses to global brands—connect with customers at the moments that matter most. Our advertising solutions enable sellers, vendors, and brand owners to grow their businesses by reaching shoppers with relevant, engaging ads across Amazon's store and beyond. We're obsessed with delivering measurable results for advertisers while creating a delightful shopping experience for customers. Are you interested in defining the science behind the future of advertising? Sponsored Products and Brands science teams are pioneering breakthrough agentic AI systems—pushing the boundaries of large language models, autonomous reasoning, planning, and decision-making to build intelligent agents that fundamentally transform how advertisers succeed on Amazon. As an SPB applied science leader, you'll have end-to-end ownership of the product and scientific vision, research agenda, model architectures, and evaluation frameworks required to deliver state-of-the-art agentic AI solutions for our advertising customers. You'll get to work on problems that are fast-paced, scientifically rich, and deeply consequential. You'll also be able to explore novel research directions, take bold bets, and collaborate with remarkable scientists, engineers, and product leaders. We'll look for you to bring your diverse perspectives, deep technical expertise, and scientific rigor to make Amazon Advertising even better for our advertisers and customers. With global opportunities for talented scientists and science leaders, you can decide where a career in Amazon Ads Science takes you! We are kicking off a new initiative within SPB to leverage agentic AI solutions to revolutionize how advertisers create, manage, and optimize their advertising campaigns. This is a unique opportunity to lead a business-critical applied science initiative from its inception—defining the scientific charter, establishing foundational research pillars, and building a multi-year science roadmap for transformative impact. As the single-threaded applied science leader, you will build and guide a dedicated team of applied scientists, research scientists, and machine learning engineers, working closely with cross-functional engineering and product partners, to research, develop, and deploy agentic AI systems that fundamentally reimagine the advertiser journey. Your charter will begin with advancing the science behind intelligent agents that simplify campaign creation, automate optimization decisions through autonomous reasoning and planning, and deliver personalized advertising strategies at scale. You will pioneer novel approaches in areas such as LLM-based agent architectures, multi-step planning and tool use, retrieval-augmented generation, reinforcement learning from human and business feedback, and robust evaluation methodologies for agentic systems. You will expand to proactively identify and tackle the next generation of AI-powered advertising experiences across the entire SPB portfolio. This high-visibility role places you as the science leader driving our strategy to democratize advertising success—making it effortless for advertisers of all sizes to achieve their business goals while delivering relevant experiences for Amazon customers. Key job responsibilities Build, mentor, and lead a new, high-performing applied science organization of applied scientists, research scientists, and engineers, fostering a culture of scientific excellence, innovation, customer obsession, and ownership. Define, own, and drive the long-term scientific and product vision and research strategy for agentic AI-powered advertising experiences across Sponsored Products and Brands—identifying the highest-impact research problems and charting a path from exploration to production. Lead the research, design, and development of novel agentic AI models and systems—including LLM-based agent architectures, multi-agent orchestration, planning and reasoning frameworks, tool-use mechanisms, and retrieval-augmented generation pipelines—that deliver measurable value for advertisers and create delightful, intuitive experiences. Establish rigorous scientific methodology and evaluation frameworks for assessing agent performance, reliability, safety, and advertiser outcomes, setting a high bar for experimentation, reproducibility, and offline-to-online consistency. Partner closely with senior business, engineering, and product leaders across Amazon Advertising to translate advertiser pain points and business opportunities into well-defined science problems, and deliver cohesive, production-ready solutions that drive advertiser success. Drive execution from research to production at scale, ensuring models and agentic systems meet high standards for quality, robustness, latency, safety, and reliability for mission-critical advertising services operating at Amazon scale. Champion a culture of scientific inquiry and technical depth that encourages bold experimentation, publication of novel research, relentless simplification, and continuous improvement. Communicate your team's scientific vision, research breakthroughs, strategy, and progress to senior leadership and key stakeholders, ensuring alignment with broader Amazon Advertising objectives and contributing to Amazon's position at the forefront of applied AI. Develop a science roadmap directly tied to advertiser outcomes, revenue growth, and business plans, delivering on commitments for high-impact research and modeling initiatives that shape the future of AI-powered digital advertising.

Amazon is looking for a motivated individual with strong statistical, analytical skills and technological experience to join the DIGI (Data Infrastructure and Generative Intelligence) Ad Sales Finance analytics team. In this position the successful candidate will be responsible for partnering with Finance and Business leaders to optimize Sales Forecasts Key job responsibilities - Build and train models to support forecasting and planning for Advertising Sales Finance - Have strong technical experience, but also be able to work with non-tech partners and communicate complex and technical topics in a simple and understandable fashion - Have a good understanding of machine learning or statistical modeling techniques, including a strong understanding of model parameters and how they affect performance - Understand time-series forecasting techniques (e.g., STL decomposition, ETS/Holt-Winters, ARIMA, Prophet, or similar models) - Familiarity with hierarchical or segmented forecasting problems (e.g., product, region, channel, or customer-level splits) - Apply theoretical or statistical models in an applied, real-world environment - Perform model evaluation such as confidence intervals, error metrics, backtesting, and validation datasets - Work with large, complex datasets across multiple dimensions - Translate analytical findings into clear, actionable insights for business stakeholders A day in the life In this position the successful candidate will be responsible for partnering with Finance and Business leaders to expand and optimize forecasting models that supports weekly, monthly, quarterly and annual reviews for the Display Ads Finance group and our stakeholders. About the team The Advertising Sales Finance Analytics & FP&A team's responsibilities comprise of corporate reporting, planning, Headcount & OpEx, Goals reporting, and ad-hoc analysis. We support Advertising leaders and finance teams by coordinating and consolidating deliverables, centralizing and standardizing processes, establishing financial controls and mechanisms, building tools that improve the speed of decision making, and providing insightful financial analysis on the short and long term strategy of Advertising.

This role will contribute to developing the Economics and Science products and services in the Fee domain, with specialization in supply chain systems and fees. Through the lens of economics, you will develop causal links for how Amazon, Sellers and Customers interact. You will be a key and senior scientist, advising Amazon leaders how to price our services. You will work on developing frameworks and scalable, repeatable models supporting optimal pricing and policy in the two-sided marketplace that is central to Amazon's business. The pricing for Amazon services is complex. You will partner with science and technology teams across Amazon including Advertising, Supply Chain, Operations, Prime, Consumer Pricing, and Finance. We are looking for an experienced Economist to improve our understanding of seller Economics, enhance our ability to estimate the causal impact of fees, and work with partner teams to design pricing policy changes. In this role, you will provide guidance to scientists to develop econometric models to influence our fee pricing worldwide. You will lead the development of causal models to help isolate the impact of fee and policy changes from other business actions, using experiments when possible, or observational data when not. Key job responsibilities The ideal candidate will have extensive Economics knowledge, demonstrated strength in practical and policy relevant structural econometrics, strong collaboration skills, proven ability to lead highly ambiguous and large projects, and a drive to deliver results. They will work closely with Economists, Data / Applied Scientists, Strategy Analysts, Data Engineers, and Product leads to integrate economic insights into policy and systems production. Familiarity with systems and services that constitute seller supply chains is a plus but not required. About the team The Stores Economics and Sciences team is a central science team that supports Amazon's Retail and Supply Chain leadership. We tackle some of Amazon's most challenging economics and machine learning problems, where our mandate is to impact the business on massive scale.

RBS (Retail Business Services) Tech team works towards enhancing the customer experience (CX) and their trust in product data by providing technologies to find and fix Amazon CX defects at scale. Our platforms help in improving the CX in all phases of customer journey, including selection, discoverability & fulfilment, buying experience and post-buying experience (product quality and customer returns). The team also develops GenAI platforms for automation of Amazon Stores Operations. As a Sciences team in RBS Tech, we focus on foundational ML research and develop scalable state-of-the-art ML solutions to solve the problems covering customer experience (CX) and Selling partner experience (SPX). We work to solve problems related to multi-modal understanding (text and images), task automation through multi-modal LLM Agents, supervised and unsupervised techniques, multi-task learning, multi-label classification, aspect and topic extraction for Customer Anecdote Mining, image and text similarity and retrieval using NLP and Computer Vision for product groupings and identifying duplicate listings in product search results. Key job responsibilities As an Applied Scientist, you will be responsible to design and deploy scalable GenAI, NLP and Computer Vision solutions that will impact the content visible to millions of customer and solve key customer experience issues. You will develop novel LLM, deep learning and statistical techniques for task automation, text processing, image processing, pattern recognition, and anomaly detection problems. You will define the research and experiments strategy with an iterative execution approach to develop AI/ML models and progressively improve the results over time. You will partner with business and engineering teams to identify and solve large and significantly complex problems that require scientific innovation. You will help the team leverage your expertise, by coaching and mentoring. You will contribute to the professional development of colleagues, improving their technical knowledge and the engineering practices. You will independently as well as guide team to file for patents and/or publish research work where opportunities arise. The RBS org deals with problems that are directly related to the selling partners and end customers and the ML team drives resolution to organization level problems. Therefore, the Applied Scientist role will impact the large product strategy, identifies new business opportunities and provides strategic direction which is very exciting.

RBS (Retail Business Services) Tech team works towards enhancing the customer experience (CX) and their trust in product data by providing technologies to find and fix Amazon CX defects at scale. Our platforms help in improving the CX in all phases of customer journey, including selection, discoverability & fulfilment, buying experience and post-buying experience (product quality and customer returns). The team also develops GenAI platforms for automation of Amazon Stores Operations. As a Sciences team in RBS Tech, we focus on foundational ML research and develop scalable state-of-the-art ML solutions to solve the problems covering customer experience (CX) and Selling partner experience (SPX). We work to solve problems related to multi-modal understanding (text and images), task automation through multi-modal LLM Agents, supervised and unsupervised techniques, multi-task learning, multi-label classification, aspect and topic extraction for Customer Anecdote Mining, image and text similarity and retrieval using NLP and Computer Vision for product groupings and identifying duplicate listings in product search results. Key job responsibilities As an Applied Scientist, you will be responsible to design and deploy scalable GenAI, NLP and Computer Vision solutions that will impact the content visible to millions of customer and solve key customer experience issues. You will develop novel LLM, deep learning and statistical techniques for task automation, text processing, image processing, pattern recognition, and anomaly detection problems. You will define the research and experiments strategy with an iterative execution approach to develop AI/ML models and progressively improve the results over time. You will partner with business and engineering teams to identify and solve large and significantly complex problems that require scientific innovation. You will independently file for patents and/or publish research work where opportunities arise. The RBS org deals with problems that are directly related to the selling partners and end customers and the ML team drives resolution to organization level problems. Therefore, the Applied Scientist role will impact the large product strategy, identifies new business opportunities and provides strategic direction which is very exciting.

RBS (Retail Business Services) Tech team works towards enhancing the customer experience (CX) and their trust in product data by providing technologies to find and fix Amazon CX defects at scale. Our platforms help in improving the CX in all phases of customer journey, including selection, discoverability & fulfilment, buying experience and post-buying experience (product quality and customer returns). As a Sciences team in RBS Tech, we focus on foundational ML research and develop scalable state-of-the-art ML solutions to solve the problems covering customer experience (CX) and Selling partner experience (SPX). We work to solve problems related to multi-modal understanding (text and visual), supervised and unsupervised techniques, multi-task learning, multi-label classification, aspect and topic extraction for Customer Anecdote Mining, product similarity, using GenAI, LLMs, NLP and Computer Vision. Key job responsibilities As an Applied Science Manager, you will be responsible to design and deploy scalable GenAI, NLP and Computer Vision solutions that will impact the content visible to millions of customer and solve key customer experience issues. You will Lead scientists on the team and oversee research and development projects at various stages ranging from initial exploration to deployment into production systems. You will partner with business and engineering teams to identify and solve large and significantly complex problems that require scientific innovation. You will help the team leverage your expertise, by coaching and mentoring. You will contribute to the professional development of colleagues, improving their technical knowledge and the engineering practices. You will create the environment in the team to file for patents and/or publish research work where opportunities arise. You will impact the large product strategy, identifies new business opportunities and provides strategic direction to the team.