Applied Scientist

Who you are

• Experience programming in Java, C++, Python or related language
• Knowledge of Machine Learning and LLM fundamentals, including transformer architecture, training/inference lifecycles, and optimization techniques
• Master's degree in a quantitative field such as statistics, mathematics, data science, business analytics, economics, finance, engineering, or computer science
• Fluency in written and spoken English (German is not required)
• Experience implementing algorithms using both toolkits and self-developed code
• Have publications at top-tier peer-reviewed conferences or journals
• Experience working on recommender systems or personalization within search, e-commerce, shopping, advertising or other related fields
• 1+ years of post-Master's hands-on experience (academic or industrial) building ML models
• Fluency in one or more languages other than English

What the job involves

• As an Applied Scientist on this team, you will take on a key role in improving capabilities of the Amazon product search service, and ensure we offer top-quality recommendations for all our customers regardless of their location or language
• Our ultimate goal is to help customers find the products they are searching for, and discover new products they would be interested in
• We do so by developing components to improve query understanding, ranking of search results, and personalization -- all covering a wide range of languages
• This is a rewarding role where you will be able to draw a clear connection between your work and how it improves the experience of millions of Amazon customers across the globe every day
• You can also expect to have excellent opportunities, and ample support, for career growth, development, and mentorship
• Analyze the data and metrics resulting from traffic into Amazon's product search service
• Design, build, and deploy effective and innovative ML solutions to improve various components of the search stack, query understanding, ranking, and personalization
• Evaluate the proposed solutions via offline benchmark tests as well as online A/B tests in production
• Publish and present your work at internal and external scientific venues in the fields of ML/NLP/IR
• You will propose and explore publication-worthy innovation using ML/NLP/IR techniques, build ML models trained on terabytes of data, which are evaluated using both offline metrics as well as online metrics via A/B testing
• You will then integrate these models into the production search engine that serves customers, closing the loop through data, modeling, application, and customer feedback. The chosen approaches for model architecture will balance business-defined performance metrics with the needs of millisecond response times