Careers

Are you a scientist interested in pushing the state of the art in Information Retrieval, Large Language Models and Recommendation Systems? Are you interested in innovating on behalf of millions of customers, helping them accomplish their every day goals? Do you wish you had access to large datasets and tremendous computational resources? Do you want to join a team of capable scientist and engineers, building the future of e-commerce? Answer yes to any of these questions, and you will be a great fit for our team at Amazon. Our team is part of Amazon’s Personalization organization, a high-performing group that leverages Amazon’s expertise in machine learning, generative AI, large-scale data systems, and user experience design to deliver the best shopping experiences for our customers. Our team builds large-scale machine-learning solutions that delight customers with personalized and up-to-date recommendations that are related to their interests. We are a team uniquely placed within Amazon, to have a direct window of opportunity to influence how customers will think about their shopping journey in the future. As an Applied Scientist in our team, you will be responsible for the research, design, and development of new AI technologies for personalization. You will adopt or invent new machine learning and analytical techniques in the realm of recommendations, information retrieval and large language models. You will collaborate with scientists, engineers, and product partners locally and abroad. Your work will include inventing, experimenting with, and launching new features, products and systems. Please visit https://www.amazon.science for more information.

Are you a scientist interested in pushing the state of the art in Information Retrieval, Large Language Models and Recommendation Systems? Are you interested in innovating on behalf of millions of customers, helping them accomplish their every day goals? Do you wish you had access to large datasets and tremendous computational resources? Do you want to join a team of capable scientist and engineers, building the future of e-commerce? Answer yes to any of these questions, and you will be a great fit for our team at Amazon. Our team is part of Amazon’s Personalization organization, a high-performing group that leverages Amazon’s expertise in machine learning, generative AI, large-scale data systems, and user experience design to deliver the best shopping experiences for our customers. Our team builds large-scale machine-learning solutions that delight customers with personalized and up-to-date recommendations that are related to their interests. We are a team uniquely placed within Amazon, to have a direct window of opportunity to influence how customers will think about their shopping journey in the future. As an Applied Scientist in our team, you will be responsible for the research, design, and development of new AI technologies for personalization. You will adopt or invent new machine learning and analytical techniques in the realm of recommendations, information retrieval and large language models. You will collaborate with scientists, engineers, and product partners locally and abroad. Your work will include inventing, experimenting with, and launching new features, products and systems. Please visit https://www.amazon.science for more information.

We are looking for a Data Scientist to join our Prime Video team in Israel, focusing on personalizing customer experiences through Search and Recommendations. Our team leverages Machine Learning (ML) to deliver tailored content discovery, helping millions of customers find the entertainment they love. You will work on large-scale experimentation, measurement frameworks, and data-driven decision-making that directly shapes how customers interact with Prime Video. Key job responsibilities - Design metrics frameworks and evaluation systems to measure the quality, performance, and reliability of algorithmic solutions - Lead the design, execution, and analysis of A/B tests to validate product hypotheses and quantify customer impact - Communicate analytical findings and recommendations clearly to both technical teams and business stakeholders, driving data-informed decisions - Partner with Applied Scientists, Software Engineers, and Product Managers to define requirements, evaluate models, and drive data-informed product decisions - Act as the subject matter expert for data structures, metrics definitions, and analytical best practices - Identify opportunities for improving customer experience through deep-dive analyses of user behavior and algorithm performance

Are you a scientist interested in pushing the state of the art in Information Retrieval, Large Language Models and Recommendation Systems? Are you interested in innovating on behalf of millions of customers, helping them accomplish their every day goals? Do you wish you had access to large datasets and tremendous computational resources? Do you want to join a team of capable scientist and engineers, building the future of e-commerce? Answer yes to any of these questions, and you will be a great fit for our team at Amazon. Our team is part of Amazon’s Personalization organization, a high-performing group that leverages Amazon’s expertise in machine learning, generative AI, large-scale data systems, and user experience design to deliver the best shopping experiences for our customers. Our team is building next-generation personalization systems powered by Large Language Models. We are tackling novel research challenges to help customers discover products they'll love - at Amazon scale and latency requirements. We are a team uniquely placed within Amazon, to have a direct window of opportunity to influence how customers will think about their shopping journey in the future. As an Applied Science Manager, you will lead a team of scientists working at the frontier of LLM-based personalization. You will set the technical vision, drive the research agenda, and ensure your team delivers production-ready solutions. You will hire, mentor, and develop world-class scientists while fostering a culture of innovation and scientific rigor. You will partner closely with engineering and product teams to translate ambitious research into customer-facing impact, and represent your team's work to senior leadership. Please visit https://www.amazon.science for more information.

Are you a MS or PhD student interested in a 2026 internship in the field of machine learning, deep learning, generative AI, large language models, speech technology, robotics, computer vision, optimization, operations research, quantum computing, automated reasoning, or formal methods? If so, we want to hear from you! We are looking for students interested in using a variety of domain expertise to invent, design and implement state-of-the-art solutions for never-before-solved problems. You can find more information about the Amazon Science community as well as our interview process via the links below; https://www.amazon.science/ https://amazon.jobs/content/en/career-programs/university/science https://amazon.jobs/content/en/how-we-hire/university-roles/applied-science Key job responsibilities As an Applied Science Intern, you will own the design and development of end-to-end systems. You’ll have the opportunity to write technical white papers, create roadmaps and drive production level projects that will support Amazon Science. You will work closely with Amazon scientists and other science interns to develop solutions and deploy them into production. You will have the opportunity to design new algorithms, models, or other technical solutions whilst experiencing Amazon’s customer focused culture. The ideal intern must have the ability to work with diverse groups of people and cross-functional teams to solve complex business problems. A day in the life At Amazon, you will grow into the high impact person you know you’re ready to be. Every day will be filled with developing new skills and achieving personal growth. How often can you say that your work changes the world? At Amazon, you’ll say it often. Join us and define tomorrow. Some more benefits of an Amazon Science internship include; • All of our internships offer a competitive stipend/salary • Interns are paired with an experienced manager and mentor(s) • Interns receive invitations to different events such as intern program initiatives or site events • Interns can build their professional and personal network with other Amazon Scientists • Interns can potentially publish work at top tier conferences each year About the team Applicants will be reviewed on a rolling basis and are assigned to teams aligned with their research interests and experience prior to interviews. Start dates are available throughout the year and durations can vary in length from 3-6 months for full time internships. This role may available across multiple locations in the EMEA region (Austria, Estonia, France, Germany, Ireland, Israel, Italy, Jordan, Luxembourg, Netherlands, Poland, Romania, South Africa, Spain, Sweden, UAE, and UK). Please note these are not remote internships.

Come join the AWS Agentic AI science team in building the next generation models for intelligent automation. AWS, the world-leading provider of cloud services, has fostered the creation and growth of countless new businesses, and is a positive force for good. Our customers bring problems that will give Applied Scientists like you endless opportunities to see your research have a positive and immediate impact in the world. You will have the opportunity to partner with technology and business teams to solve real-world problems, have access to virtually endless data and computational resources, and to world-class engineers and developers that can help bring your ideas into the world. As part of the team, we expect that you will develop innovative solutions to hard problems, and publish your findings at peer reviewed conferences and workshops. We are looking for world class researchers with experience in one or more of the following areas - autonomous agents, API orchestration, Planning, large multimodal models (especially vision-language models), reinforcement learning (RL) and sequential decision making.