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Amazon Applied Scientist III - LLMs NLP Pricing Intelligence 
United States, Washington, Seattle 
208848371

19.06.2024
DESCRIPTION

As a Senior Applied Scientist specializing in Large Language Models (LLMs) and Natural Language Processing (NLP) in the Product Intelligence team, you will lead the development of machine learning solutions. Your work will leverage the latest LLMs and multimodal models to enhance product graphs, measure entity similarity, perform entity linking, and attribute normalization, and apply advanced reasoning methods for a deeper understanding of products. You will also lead research projects to tackle unsolved problems, mentor interns, and author academic papers to summarize your findings for external publication.We seek an experienced scientist with deep knowledge of LLMs and NLP, and a good understanding of traditional machine learning and quantitative methods. The role will also require cross-functional collaboration skills, and staying up to date with the latest advancements in Generative AI.Key job responsibilities
- Implement and deploy systems that leverage LLMs and other techniques to address some of hardest problems in the pricing space.- Lead research projects and participate in the publication of external academic papers at top conferences and journals.
Seattle, WA, USA


BASIC QUALIFICATIONS

- 5+ years of applied research experience
- 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
- Expertise in deep learning and/or LLM methods
- A track record of balancing project delivery with longer term research, demonstrated via publications, or contributions to the open source community.
- Proficiency in programming languages such as Python and frameworks like TensorFlow, PyTorch
- Proven written and verbal technical communication with the ability to present complex topics clearly to audiences with varying levels of technical familiarity