Careers

Conversational AI ModEling and Learning (CAMEL) team is part of Amazon Devices organization where our mission is to build a best-in-class Conversational AI that is intuitive, intelligent, and responsive, by developing superior Large Language Models (LLM) solutions and services which increase the capabilities built into the model and which enable utilizing thousands of APIs and external knowledge sources to provide the best experience for each request across millions of customers and endpoints. We are looking for a passionate, talented, and resourceful Applied Scientist in the field of LLM, Artificial Intelligence (AI), Natural Language Processing (NLP), Recommender Systems and/or Information Retrieval, to invent and build scalable solutions for a state-of-the-art context-aware conversational AI. A successful candidate will have strong machine learning background and a desire to push the envelope in one or more of the above areas. The ideal candidate would also have hands-on experiences in building Generative AI solutions with LLMs, enjoy operating in dynamic environments, be self-motivated to take on challenging problems to deliver big customer impact, moving fast to ship solutions and then iterating on user feedback and interactions. Key job responsibilities As an Applied Scientist, you will leverage your technical expertise and experience to collaborate with other talented applied scientists and engineers to research and develop novel algorithms and modeling techniques to reduce friction and enable natural and contextual conversations. You will analyze, understand and improve user experiences by leveraging Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in artificial intelligence. You will work on core LLM technologies, including Prompt Engineering and Optimization, Supervised Fine-Tuning, Learning from Human Feedback, Evaluation, Self-Learning, etc. Your work will directly impact our customers in the form of novel products and services.

Join Amazon's International Seller Services (ISS) Economics team and shape how millions of sellers succeed in the worldwide marketplace through causal inference, LLMs, and generative AI. You'll combine rigorous economic methods with real business impact, building the frameworks and measurement systems that define the future of Amazon's seller services. Key job responsibilities In this role, you will be a people manager and a technical leader in Econometric research with significant scope, impact, and high visibility. You will own developing the economics charter in ISS combining scientific methods with business needs. Your solution will deliver to business leaders accurate and actionable insights and recommendations to optimize and expand their business. As a successful Science Manager, you can navigate ambiguity, lead problem solving, guide development of new frameworks, and credibly interface between technical teams and business stakeholders. You are an innovator who can push the limits on what’s scientifically possible with a razor sharp focus on measurable business impact. You will coach and guide scientists in your team across different job families including Economists, Data Scientists and Applied Scientists to grow the team’s talent and scale the impact of your work. About the team The International Seller Services (ISS) Economics team shapes Amazon's global seller ecosystem through sophisticated economic analysis and data-driven insights. Our mission is to transform how Amazon empowers millions of sellers to succeed in the worldwide digital marketplace.

We are embarking on a multi-year journey to improve the shopping experience for customers globally. Amazon Search team creates customer-focused search solutions and technologies that make shopping delightful and effortless for our customers. Our goal is to understand what customers are looking for in whatever language happens to be their choice at the moment and help them find what they need in Amazon's vast catalog of billions of products — starting from the very first keystroke. As Amazon expands to new interfaces, we are faced with the unique challenge of maintaining the bar on Search Assistance and Search Quality. We are looking for a Senior Applied Scientist to work on improving search on Amazon using NLP, ML, and DL technology. As an Applied Scientist, you will lead our efforts in search autocomplete — developing high-quality suggestions. You will build systems that anticipate search query intent and surface the right suggestions and results. As part of this role, you will develop high precision, high recall, and low latency solutions for search. Your solutions should work for all languages that Amazon supports and will be used in all Amazon locales world-wide. You will develop scalable science and engineering solutions that work successfully in production. You will work with leaders to develop a strategic vision and long term plans to improve search globally. We are growing our collaborative group of engineers and applied scientists by expanding into new areas. Key job responsibilities As an Applied Scientist on the team, you will lead science innovation to improve the customer search experience through higher-quality autocomplete search results. You will: - Develop and deploy ML models to produce high-quality, diverse search autocomplete suggestions. - Design and train semantic matching models (bi-encoders, cross-encoders, and distillation from large foundation models) for suggestion ranking and relevance. - Develop reinforcement learning and reward-modeling approaches to continuously improve suggestion quality. - Train multi-objective ranking and scoring systems that balance suggestion diversity, specificity, and relevance. - Design and implement scalable model architectures optimized for strict latency constraints, including knowledge distillation, quantization, and efficient inference strategies for production deployment. - Lead end-to-end science projects from problem formulation through production launch, mentoring scientists and collaborating closely with engineers and scientists within and outside the team to deliver customer-facing impact.

We are looking for a passionate, talented, and inventive Data Scientist with a strong machine learning and analytics background to help build industry-leading language technology powering Alexa for Shopping, our AI-driven search and shopping assistant, helping customers with their shopping tasks at every step of their shopping journey. This innovative role focuses on developing conversation-based, multimodal shopping experiences, utilizing data analysis, statistical modeling, machine learning (ML) technologies, and experimentation to drive product decisions and optimize customer experiences. Our mission in conversational shopping is to make it easy for customers to find and discover the best products to meet their needs by helping with their product research, providing comparisons and recommendations, answering product questions, enabling shopping directly from images or videos, providing visual inspiration, and more. We do this by leveraging advanced analytics, Natural Language Processing (NLP), Machine Learning (ML), A/B testing, causal inference, and data-driven insights to continuously improve our systems. Key job responsibilities Key job responsibilities As a Data Scientist on our team, you will be responsible for the analysis, modeling, and optimization of AI technologies that will shape the future of shopping experiences. You will play a critical role in measuring and improving multimodal conversational systems, in particular those based on large language models, information retrieval, recommender systems and knowledge graphs, to be tailored to customer needs. You will handle Amazon-scale use cases with significant impact on our customers' experiences. You will collaborate with scientists, engineers, and product partners locally and abroad. Your work will include designing experiments, analyzing results, and launching new features, products and systems. A day in the life Perform hands-on analysis and modeling of enormous multimodal datasets to develop insights into how to best help customers throughout their shopping journeys. Use statistical methods, machine learning, and data mining techniques to create scalable solutions for measuring and optimizing shopping assistant systems based on a rich set of structured and unstructured contextual signals. Design and analyze A/B tests and experiments to evaluate new features and model improvements, ensuring statistical rigor and actionable insights. Develop metrics, dashboards, and reporting frameworks to monitor system performance, customer engagement, and business impact. Build predictive models and conduct deep-dive analyses to identify opportunities for improving customer experience, conversion, and satisfaction. Collaborate with Applied Scientists and Engineers to translate analytical insights into production systems, working closely on model evaluation and deployment. Establish automated processes for large-scale data analysis, ETL pipelines, metric generation, and experimentation frameworks. Communicate results and insights to both technical and non-technical audiences, including through presentations, written reports, and data visualizations. About the team The Alexa for Shopping Science team, based in London, works alongside ~150 engineers, designers and product managers, shaping the future of AI-driven shopping experiences at Amazon. The team works on every aspect of the AfS AI, from making it agentic, enabling customers to set price alerts or empower AfS to act on their behalf and automatically purchase products when the price is right, to understanding multimodal user queries and generating answers that combine text, image, audio and video, including deep research reports that scour the web and the Amazon catalog to provide detailed and personalised shopping guidance. We utilize and advance state-of-art techniques in the fields of Natural Language Processing, gen AI, Information Retrieval, Machine/Deep Learning, and Data Mining. We validate our work by actively participating in the internal and external scientific communities.

Are you passionate to join an innovative team of scientists and engineers who use machine learning and AI techniques to create state-of-the-art solutions to help seller succeed on Amazon? The Selling Partner Selection Success org is looking for a Principal Applied Scientist to lead us on our mission to provide selection success support on Amazon, and empower sellers to grow their business and provide a great customer experience. As a Principal Applied Scientist on our team of scientists and engineers, you will have opportunities to create significant impact on our systems, our business and most importantly, our customers as we take on challenges that can revolutionize the e-commerce industry. You will identify specific and actionable opportunities to solve business problems, propose state-of-the-art solutions and collaborate with engineering, and business teams for future innovation. You need to be a great translation between ambiguous business domains and rigorous scientific solutions, an expert at inventing and simplify, and a good communicator to surface insights and recommendations to audiences of varying levels of technical sophistication. Key job responsibilities - Drive and advocate technical vision that drives the significant business impact - Use machine learning and AI techniques to create scalable seller-facing solutions - Analyze and extract relevant information from large amounts of Amazon's historical business data to help automate and optimize key processes - Design, development and evaluation of highly innovative models - Work closely with software engineering teams to drive real-time model implementations and new feature creations

We are seeking an Applied Scientist to lead the development of evaluation frameworks and data collection protocols for robotic capabilities. In this role, you will focus on designing how we measure, stress-test, and improve robot behavior across a wide range of real-world tasks. Your work will play a critical role in shaping how policies are validated and how high-quality datasets are generated to accelerate system performance. You will operate at the intersection of robotics, machine learning, and human-in-the-loop systems, building the infrastructure and methodologies that connect teleoperation, evaluation, and learning. This includes developing evaluation policies, defining task structures, and contributing to operator-facing interfaces that enable scalable and reliable data collection. The ideal candidate is highly experimental, systems-oriented, and comfortable working across software, robotics, and data pipelines, with a strong focus on turning ambiguous capability goals into measurable and actionable evaluation systems. Key job responsibilities - Design and implement evaluation frameworks to measure robot capabilities across structured tasks, edge cases, and real-world scenarios - Develop task definitions, success criteria, and benchmarking methodologies that enable consistent and reproducible evaluation of policies - Create and refine data collection protocols that generate high-quality, task-relevant datasets aligned with model development needs - Build and iterate on teleoperation workflows and operator interfaces to support efficient, reliable, and scalable data collection - Analyze evaluation results and collected data to identify performance gaps, failure modes, and opportunities for targeted data collection - Collaborate with engineering teams to integrate evaluation tooling, logging systems, and data pipelines into the broader robotics stack - Stay current with advances in robotics, evaluation methodologies, and human-in-the-loop learning to continuously improve internal approaches - Lead technical projects from conception through production deployment - Mentor junior scientists and engineers

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! As an Applied Scientist, you will apply state of the art natural language processing and computer vision research to video centric digital media. We are looking for scientists with expertise in vision-language models/multimodal LLMs and long-form content understanding (full movies/episode vs. short clips). You will be dealing with architectures that handle long-context understanding and causal reasoning across extended temporal sequences. Key job responsibilities Our team builds multi-modal machine learning technologies to enrich and understand video content. We aim not only to understand individual components within the content itself, but also their relationships to each other to provide a holistic and broader contextual understanding. This powers the next generation of video understanding and search capabilities for Prime Video. About the team Prime Video's Content Localization, Understanding & Enrichment organization is responsible for 1) enabling Prime Video to "see" and "understand" video content including characters, scenes, dialogue, events & visual elements and 2) delivering localized, accessible content that meets a consistent cinematic quality standard at scale. This team's mission is to deeply understand all content and empower all customers with relevant language options, innovative accessibility assists, and rich title-information across all their content-experiences on Prime Video. We create and publish content on-time that's meaningful, accurate, and accessible to every customer globally. We delight our customers by pushing the boundaries of content understanding and enrichment. Through inclusion and innovation, we do the most fulfilling work of our career.

The AI Center of Excellence (AICE) builds AI primitives that power system-intrinsic intelligence and Trusted Intelligent Knowledge Infrastructure for both AI-as-a-Consumer and human users. We develop AI capabilities independently and in partnership with product-owning teams to evolve Amazon's AI-driven operations and productivity. Our impact is enterprise-broad and global. We are looking for an Applied Scientist who can identify high-impact problems, design rigorous experiments, and translate research into hardened, reusable primitives ready for broad consumption across the enterprise. Key job responsibilities * Research, design, and develop AI/ML primitives - defining problem spaces, formulating approaches, running experiments, and validating outcomes with scientific rigor * Translate research findings into production-ready primitives, working closely with machine learning engineers to harden and scale solutions * Drive the scientific direction of AICE primitives, identifying opportunities where novel approaches can unlock step-function improvements * Design and execute experiments to validate primitive performance, robustness, and generalizability before broad consumption by partner teams * Collaborate with product-owning teams to understand their problem spaces and ensure AICE primitives deliver measurable value when integrated * Contribute to AICE's mission of driving AI transformation through best practices in applied research, evaluation methodology, and responsible AI within your area of expertise * Publish findings, share learnings, and raise the scientific bar across the team A day in the life Every day brings new challenges. You might be deep in experimentation - designing evaluations to stress-test a new primitive's robustness. The next day, you could be analyzing results and iterating on model architectures. Later in the week, you may collaborate with partners to understand their domain and shape a primitive that fits their product needs. Other days, you'll work alongside our MLEs to harden a validated approach for broader consumption. No two weeks look the same. You'll move fluidly between research and applied problem-solving - always with the goal of building AI primitives that are scientifically sound, trusted, and impactful at enterprise scale. About the team AICE is a team of scientists and machine learning engineers building the AI primitives that power Amazon's intelligent systems. We develop and harden reusable capabilities for broad consumption by product teams across the enterprise - and partner with those teams to integrate them.

We are seeking an Economist to join our growing team in Japan. In this role, you will apply rigorous economic and econometric methods to guide critical business decisions affecting Amazon's JP marketplace. You will build causal inference models to measure the impact of business initiatives on pricing, product selection, delivery speed, profitability, and customer experience. Working alongside economists, data scientists, and business intelligence engineers, you will tackle challenging problems using state-of-the-art analytical techniques while providing advisory support to business stakeholders. As one of the first economists based outside North America and EU, you will play a pioneering role in expanding Amazon's economist community in Asia and make an outsized impact on our international marketplace operations. At Amazon, you'll work alongside the latest AI and GenAI tools that are increasingly woven into how teams operate: from AI-powered capabilities that accelerate decision-making, to Generative AI that helps you focus on work that truly matters. You'll have opportunities and resources to develop AI fluency at your own pace, with continuous learning built into the culture. Key job responsibilities Design and execute causal inference analyses using econometric techniques to measure the impact of business initiatives on key marketplace metrics Build economic models to optimize pricing strategies, product selection decisions, and delivery speed investments that balance customer experience with business profitability Collaborate with product managers, engineers, and business leaders to translate complex business questions into tractable research problems and deliver actionable insights Design and analyze experiments to test hypotheses and validate causal relationships in observational data Develop scalable analytical frameworks and tools using R, Python, or Stata that can be leveraged across multiple business use cases Present findings and recommendations to technical and non-technical audiences, including senior leadership, through clear written narratives and data visualizations Partner with Machine Learning and BI team members to integrate economic insights into automated decision-making systems About the team The JP Economics and Decision Sciences team is a central science team that applies rigorous economic theory, causal inference methods, and machine learning to solve complex business challenges across the JP marketplace and beyond. We work closely with JP business leaders to drive change at Amazon, focusing on solving long-term, ambiguous problems while providing advisory support for short-term business pain points. Key topics include pricing, product selection, delivery speed, profitability, and customer experience. We tackle these issues by building novel economic and econometric models, machine learning systems, and high-impact experiments which we integrate into business, financial, and system-level decision making. Our work is highly collaborative and we regularly partner with JP-, EU-, and US-based interdisciplinary teams.

MULTIPLE POSITIONS AVAILABLE Employer: Amazon Development Center U.S., Inc. Offered Position: Applied Scientist III - AMZ007408 Job Location: New York, NY Position Responsibilities: Participate in the design, development, evaluation, deployment, and updating of formal reasoning systems for security, privacy, and data protection applications. Drive technical and scientific innovation in security automation, data protection, and privacy-preserving technologies, with a focus on developing scalable solutions for cloud environments. Develop and/or apply formal verification techniques and automated theorem proving methods for different applications in cloud security and privacy. Collaborate with internal and external users to understand requirements and enhance formal verification and automated reasoning capabilities. Lead research and development efforts in AI security, specifically evaluate emerging threats and opportunities, including securing Generative AI systems and designing robust safeguards. Proactively identify and explore new opportunities for deploying and leveraging formal reasoning solutions across various domains.