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Description

As an Applied Scientist in the Product Intelligence team, you will work on ML solutions leveraging custom DNN architectures, LLMs, and Agentic workflows. 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 participate 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 a scientist with knowledge of Deep Learning, 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 retail pricing space.

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

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

About the team

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

Basic Qualifications

- PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience

- Experience programming in Java, C++, Python or related language

- Have publications at top-tier peer-reviewed conferences or journals

Preferred Qualifications

- - Hands-on experience with LLMs and

- fine-tuning Language Models.

- - Experience with NLP tasks or multimodal models.

- - Knowledge of graph structures, GNNs, and knowledge bases.

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, 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 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.

Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $136,000/year in our lowest geographic market up to $223,400/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 https://www.aboutamazon.com/workplace/employee-benefits . This position will remain posted until filled. Applicants should apply via our internal or external career site.