Description Are you ready to make a global impact with your expertise in Machine Learning and Statistics? Amazon is seeking an innovative and driven Applied Scientist to join our team and help shape the future of cutting-edge technology. In this role, you will design, develop, and deploy state-of-the-art machine learning models to address some of the most complex and meaningful challenges in the digital world. Your work will directly enhance the experiences of millions of customers on the world’s largest online retail platform. As a member of the Operations Risk and Compliance (ORC) Science team, your mission will be to revolutionize product classification for every item sold on Amazon’s platform, ensuring seamless compliance with public authority regulations worldwide. Our work is at the intersection of cutting-edge machine learning and real-world impact, driving Amazon's commitment to operational excellence. Our team leverages a diverse range of machine learning methodologies, from gradient-boosting algorithms to state-of-the-art transformer-based architectures. These models process complex data streams to deliver accurate and efficient product classifications at scale. Our ambitious vision is to achieve 100% automation with 0% risk, eliminating manual intervention while ensuring the highest levels of precision and scalability. As a research-focused team, we are dedicated to experimentation and innovation. By integrating multiple data modalities—images, text-based attributes, and categorical data—we aim to develop robust and reliable multimodal models that push the boundaries of what is possible in automated product classification. Through rigorous testing, we continuously refine our approaches to enhance accuracy and adapt to the dynamic needs of a global marketplace. Our commitment to advancing the field extends beyond practical applications. We actively contribute to the scientific community by publishing research in leading conferences such as ICML, NeurIPS, and other Amazon-hosted events. By sharing our findings, we aim to validate the novelty of our methods, inspire the broader community, and remain at the forefront of machine learning innovation. If you’re passionate about innovation, thrive in a dynamic environment, and want to leave your mark on the future of e-commerce, we want to hear from you. Join us in transforming the way the world works with machine learning. This is your opportunity to collaborate with world-class scientists and engineers, advancing innovation in machine learning, natural language processing, and statistical modeling. Your contributions will extend beyond technology—transforming research into scalable solutions that redefine customer experience and operational efficiency. Whether publishing scientific papers, developing patents, or presenting your work to stakeholders, you’ll have a platform to showcase your expertise while driving real-world impact. Key Responsibilities: Conduct pioneering research in machine learning, statistics, and multimodal systems to create groundbreaking solutions for customer and operational challenges. Develop high-performance, production-ready code optimized for large-scale, high-traffic applications. Analyze vast datasets to uncover actionable insights, design scalable algorithms, and seize opportunities for innovation. Validate machine-learning models through rigorous statistical experiments involving millions of users. Partner with software engineering teams to prototype and integrate successful models into global production systems. Collaborate with a multidisciplinary team of applied scientists and engineers to push the boundaries of innovation. Publish research papers at Amazon-hosted and external conferences (e.g., ICML, NeurIPS) to validate and showcase the novelty of the methods developed. About the Location: This role is based in Luxembourg, home to Amazon’s European headquarters. Nestled in the heart of Europe and bordered by France, Belgium, and Germany, Luxembourg is a vibrant, multicultural hub offering a high standard of living and easy access to major European cities. Known for its thriving financial and tech sectors, Luxembourg combines innovation with a rich cultural scene and stunning natural landscapes. Discover more about life in Luxembourg at Promote Luxembourg. Key job responsibilities Drive innovation in machine learning by designing and developing cutting-edge models to accurately classify products on Amazon’s global platform. Harness the power of multimodal approaches, combining text, images, and categorical data, along with advanced transformer architectures and computer vision techniques, to deliver state-of-the-art solutions that set new standards for accuracy, scalability, and efficiency. Collaborate closely with Software Development Engineers to seamlessly integrate machine learning models into production systems, ensuring they meet the stringent latency requirements necessary to process millions of events per day. Address engineering challenges by developing innovative workarounds and optimizations, enabling the efficient deployment and scalability of these models in real-world environments. Contribute to the scientific community by authoring groundbreaking research papers that tackle complex business problems in product classification. Showcase the novelty and practical applications of your methods at renowned conferences such as ICML, NeurIPS, and CVPR, driving forward both the academic and industrial applications of machine learning. A day in the life A Day in the Life of an Applied Scientist II at Amazon As an Applied Scientist II at Amazon, each day is a blend of technical challenges, innovation, and collaboration. Here’s a glimpse into a typical day: 8:30 AM – Start the Day with Focus The day begins with a quick check-in on emails, project updates, and pending code reviews. Any blockers flagged by the team are prioritized to keep progress smooth. 9:00 AM – Team Stand-Up Join the daily stand-up meeting with scientists, engineers, and stakeholders. Share updates on experiments, discuss challenges, and align on priorities. The collaborative environment ensures everyone is on the same page. 10:00 AM – Deep Dive into Research Dive into ongoing research tasks. This could mean reading the latest papers on transformers or multimodal learning, brainstorming ways to integrate new ideas into Amazon’s systems, or designing experiments to test novel approaches. 12:00 PM – Lunch and Networking Take a break to recharge. Amazon offers opportunities to connect with peers over lunch, whether it’s discussing industry trends or exploring ideas for cross-team collaboration. 1:00 PM – Model Development and Experimentation Head into coding and experimentation mode. You might be refining a multimodal model for product classification, training transformers on massive datasets, or fine-tuning a computer vision model to improve prediction accuracy. Metrics are monitored closely to ensure models meet Amazon's high standards. 3:30 PM – Collaboration Time Meet with engineers to optimize deployment strategies for models in production. Discussions often focus on meeting latency requirements for real-time inference without compromising accuracy. This synergy between science and engineering is key to delivering scalable solutions. 4:30 PM – Results Review and Insights Analyze experimental results, document findings, and prepare for upcoming iterations. Insights gained here shape the next steps, whether it’s tweaking hyperparameters or exploring a new approach entirely. 5:30 PM – Wrap-Up Before wrapping up, check in with teammates on Slack or work on updating project documentation. Reflect on the day’s achievements and prepare a to-do list for tomorrow. 6:00 PM – Personal Development and Learning Amazon values continuous learning, so evenings might include taking a course, attending a tech talk, or participating in a knowledge-sharing session within the team. As an Applied Scientist II at Amazon, no two days are exactly the same, but every day is filled with opportunities to solve challenging problems, contribute to impactful projects, and grow as a scientist. Basic Qualifications - PhD, or a Master's degree and experience in CS, CE, ML or related field - Experience in building models for business application - Experience in patents or publications at top-tier peer-reviewed conferences or journals - Experience programming in Java, C++, Python or related language - Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing Preferred Qualifications - Experience using Unix/Linux - Experience in professional software development Amazon is an equal opportunities employer. We believe passionately that employing a diverse workforce is central to our success. We make recruiting decisions based on your experience and skills. We value your passion to discover, invent, simplify and build. Protecting your privacy and the security of your data is a longstanding top priority for Amazon. Please consult our Privacy Notice ( https://www.amazon.jobs/en/privacy\_page ) to know more about how we collect, use and transfer the personal data of our candidates. Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.