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.

This high-impact role is critical to our core business, influencing the reliability of information for billions of products on Amazon's platform, and impacting the shopping journey for hundreds of millions of customers. The systems you build will be used to monitor Amazon's entire product selection to ensure their quality, the availability of accurate price distributions, and more.

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.

- Set scientific standards and see the big picture to influence Amazon's long-term vision for retail pricing science.

- Work cross-functionally with various teams to align machine learning initiatives with business goals and execute them successfully.

- Lead research projects and participate in the publication of external academic papers at top conferences and journals.

About the team
Retail pricing science is a centralized diverse team of STEM scientists that develop statistical, ML, RL, optimization and economic models that drive pricing for products sold by Amazon worldwide, as well as monitoring of prices and experimentations in pricing. The team has a dual focus on competitiveness and long term financial optimality.

- PhD, or Master's degree and 10+ years of applied research experience
- 4+ years of applied research experience
- 4+ years of building machine learning models for business application experience
- Experience programming in Java, C++, Python or related language
- Experience with neural deep learning methods and machine learning

- PhD in engineering, technology, computer science, machine learning, robotics, operations research, statistics, mathematics or equivalent quantitative field
- Experienced with ML techniques in anomaly an error detection. Strong hands on background with deep learning frameworks such as TensorFlow or pytorch. Familiarity with weak supervision and semi-supervised techniques.

Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status.

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 for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.

Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $150,400/year in our lowest geographic market up to $260,000/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit This position will remain posted until filled. Applicants should apply via our internal or external career site.