We are looking for a student enrolled in a Japanese university to work on applying large language models (LLM) to customer recommendation problems. The role is for 1-3 months with possibility to extend.
For this fellowship, you will complete an LLM project on recommendation and customer targeting. This requires you to build and validate data pipelines, perform extensive data cleaning and exploration, train and evaluate your models in a robust manner, design and conduct A/B tests to validate model performance, and automate model inference on AWS infrastructure. A mentor Applied Scientist will work with you to define the objectives and scope of the project, as well as provide regular feedback and consultation on a weekly basis.
The ideal candidate has built and validated a machine learning model for natural language processing (NLP) and tabular data. They must have strong skills in Python and its data analysis packages e.g. Numpy, Pandas, Matplotlib/Seaborn/Plotnine, as well as commonly used LLM packages e.g. HuggingFace. They have excellent understanding of how the ML models they train work under the hood. In addition, they may have worked with AWS infrastructure, distributed computing frameworks e.g. PySpark/SparkSQL.
The fellowship will be carried out from our Tokyo office in Meguro together with the rest of the team. Amazon will provide the necessary IT equipment (laptop, etc.) for the duration of the fellowship, a salary, and a stipend to cover commuting expenses.
A day in the life
About the team
Economics and Decision Science Team is an embedded science team in the JP marketing space. Our ML models in recommendation and customer targeting directly interact with customers, creating immediate and long-lasting impact to their experience on Amazon.
Basic Qualifications
Preferred Qualifications
The salary information can be provided individually prior to the 1st interview
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By working together on behalf of our customers, we are building the future one innovative product, service, and idea at a time. Are you ready to embrace the challenge? Come build the future with us.
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