Senior Applied Scientist, Catalog AI, Amazon Selection and Catalog Systems

Senior Applied Scientist, Catalog AI, Amazon Selection and Catalog Systems Job ID: 3121716 | Amazon.com Services LLC 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. We develop LLM applications that make Catalog the best‑in‑class source of product information for all products worldwide. This problem is challenging due to sheer scale (billions of products in the catalog), diversity (products ranging from electronics to groceries) and multitude of input sources (millions of sellers contributing product data with different quality). We are seeking a passionate, talented, and inventive individual to join the Catalog AI team and help build industry‑leading technologies that customers will love. You will apply machine learning and large language model techniques, such as fine‑tuning, reinforcement learning, and prompt optimization, to solve real customer problems. You will work closely with scientists and engineers to experiment with new methods, run large‑scale evaluations, and bring research ideas into production. Key job responsibilities Design and implement LLM‑based solutions to improve catalog data quality and completeness Conduct experiments and A/B tests to validate model improvements and measure business impact Optimize large language models for quality and cost on catalog‑specific tasks Collaborate with engineering teams to deploy models at scale serving billions of products Basic Qualifications 5+ years of building machine learning models for business application experience 8+ years of applied research 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 Preferred Qualifications Experience with modeling tools such as R, scikit‑learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy, etc. Experience with large scale distributed systems such as Hadoop, Spark, etc. Experience researching machine learning, deep learning, NLP, computer vision, data science Experience with Large Language Model (LLM) fine‑tuning, prompt engineering, distillation, cost‑optimization or reinforcement learning techniques 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 $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 https://www.aboutamazon.com/workplace/employee-benefits. This position will remain posted until filled. Applicants should apply via our internal or external career site. #J-18808-Ljbffr