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Description

Are you fascinated by the power of Large Language Models (LLM) and applying Generative AI to solve complex challenges within one of Amazon's most significant businesses? Amazon Selection and Catalog Systems (ASCS) builds the systems that host and run the world’s largest e-Commerce products catalog - it powers the online buying experience for customers worldwide so they can find, discover and buy anything they want. Amazon’s customers rely on the completeness, consistency and correctness of Amazon's product data to make well-informed purchase decisions. Improving the quality of product data is a continuous process. It requires data driven decisions on what product data changes simplify and improve the Customers’ experience.

Amazon Selection and Catalog Systems (ASCS) is seeking an Applied Scientist II for developing Generative AI and Large Language Models (LLM) that can help generate richer and expanded product information. You will be a part of a team consisting of experienced Applied Scientists working on a new set of initiatives, building models and delivering them into the Amazon production ecosystem. Your efforts will build a robust ensemble of LLM artifacts and ML systems that can achieve high precision and recall, and scale to new marketplaces and languages. This problem is challenging due to sheer scale (billions of products in the catalog), diversity (products ranging from electronics to groceries to instant video across multiple languages) and multitude of input sources (millions of sellers contributing product data with different quality). We are looking for an experienced Scientist who can develop best in class solutions. Your primary customers are Amazon shoppers who would thank you for correct product information in our catalogs across countries and languages. The ideal Applied Scientist candidate has deep expertise in one or several of the following fields: Web search, Applied/Theoretical Machine Learning, Large Language Models, Deep Neural Networks, Classification Systems, Clustering, Label Propagation, Natural Language Processing, Computer Vision, Active learning, and Artificial Intelligence. S/he has a strong publication record at top relevant academic venues and experience in launching products/features in the industry. Do you want the excitement of experimenting with cutting edge Large Language Models (LLMs), machine learning, natural language processing, computer vision, and active learning models to solve real world problems at scale? Imagine experimenting with LLMs, with Deep Neural Networks as your daily job and imagine using your model outputs to affect the product discovery of the biggest e-commerce retailer in the world. Imagine doing research inside of an Amazon team that is always looking to deploy creative solutions to real world problems in product discovery. Your research findings are directly related to Amazon’s customer experiences and impact millions of customers, ingesting images, text and all the structured and unstructured attributes in the Amazon catalog to drive true understanding of products at scale. Amazon Science gives you insight into the company’s approach to customer-obsessed scientific innovation. Amazon fundamentally believes that scientific innovation is essential to being the most customer-centric company in the world. It’s the company’s ability to have an impact at scale that allows us to attract some of the brightest minds in artificial intelligence and related fields. Our scientists continue to publish, teach, and engage with the academic community, in addition to utilizing our working backwards method to enrich the way we live and work. Please visit https://www.amazon.science for more information.

A day in the life

You will work with an Amazon team that builds creative solutions to real world problems. Your team will own devising the strategy and execution plans that power initiatives ranging from: developing tuning artifacts on top of foundational LLMs, training ML models, performing fact extraction, automatic detection of missing product information, active learning mechanisms for scaling human tasks, building applications for distilling product information, building mechanisms to analyze product composition, ingest images, text, and unstructured data to drive deep understanding of products at scale.

About the team

The team's mission is to infer knowledge, understand, classify, derive product facts for all Amazon products entering the Catalog. The work is critical to power the Amazon product detail pages experiences, Search, Navigation and impacting millions of customers. This is an already formed team with experience leading programs spanning services and ML initiatives supporting all countries and languages. The candidate collaborates closely with Scientists, Software Developers, and has exposure to multiple peer teams at Amazon who rely on this team's developments.

We are open to hiring candidates to work out of one of the following locations:

Seattle, WA, USA

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

• Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing

Preferred Qualifications

• PhD in Computer Science (Machine Learning, AI, Statistics, Electrical Engineering or equivalent);

• Experience distilling informal customer requirements into problem definitions, dealing with ambiguity and competing objectives

• Experience delivering systems into production with high precision.

• More than 4 years of industrial/academic experience in building classification models

• Extensive practical experience in several of the following areas: ML, LLM, Natural Language Processing, Recommendation Systems, Clustering techniques, applied ML or AI features/products/systems

• Ability to handle multiple competing priorities in a fast-paced environment

• Significant peer reviewed scientific contributions in premier journals and conferences

• Experience with defining research and development practices in an applied environment;

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. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.

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 $222,200/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. Applicants should apply via our internal or external career site.