Amazon Sr Applied Scientist AGI Info - Web Information Systems 

United States, California, Sunnyvale 

953742711

If you are looking for an opportunity to develop innovative solutions to deep technical problems in web-scale information retrieval (IR), helping to deliver retrieval-augmented generation solutions, having a massive customer impact, this might be the role for you! As a Applied Scientist, you will work with smart, passionate colleagues in a fast-paced environment. You will leverage your expertise in LLMs to develop a state-of-the-art evaluation and document labeling workflow to drive automated evaluations and training data generation. You will keep up with relevant research in the field of IR and publish your work in top-tier conferences. You will develop and help lead the execution of multi-year research roadmaps, enabling the team to focus on the right technical challenges to delight our customers.
Key job responsibilities
As an Sr. Applied Scientist, you will apply scientific rigor to your work. You will deliver incrementally, setting up and executing experiments informed by rigorous failure space analysis of prior experiments. You have a knack for writing clear, succinct and informative reports of your experiments. You collaborate and seek guidance from other scientists on your team to define the next experiments. You collaborate closely with engineers to bring your innovations into production. You will continuously with customers and stakeholders to simplify and adapt to deliver for our customers. You will develop state-of-the-art web search technology, including training novel retrieval and ranking models, scaling models and optimizing performance, partnering with engineering to deploy and debug model performance in production, and building and scaling quality training data sets.

- 3+ years of building machine learning models for business application experience
- PhD, or Master's degree and 6+ years of applied research experience
- Experience programming in Java, C++, Python or related language
- Experience with neural deep learning methods and machine learning