Amazon Data Scientist Fellow LLM 

Japan 

247013766

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.Key job responsibilities
- Build and validate data pipelines for training and evaluating the LLMs
- Extensively clean and explore the datasets
- Train and evaluate LLMs in a robust manner
- Design and conduct A/B tests to validate model performance
- Automate model inference on AWS infrastructureA day in the life
- On days of your internship at Meguro office, you usually start at 9-10 am and finish around 6-7 pm. You can flexibly determine your working hours as long as you are present for the meetings that require your attendance.
- Once a week, you will have a consultation with your mentor to update progress, get feedback, and suggestions for the project. You may schedule additional meetings or directly reach out to the mentor as necessary.
- Your mentor will work with you to set project milestones; you plan and execute the tasks to reach them at your own pace.
Tokyo, 13, JPN

- Currently enrolled in a Master's or PhD program in Computer Science, Data Science, Statistics, or a related field.
- Proficiency in Python programming language.
- Familiarity with large language model development and libraries (HuggingFace, LlamaIndex, Pytorch).

- Experience with natural language processing (NLP) techniques and libraries (e.g., NLTK, spaCy).
- Knowledge of deep learning frameworks and architectures.
- Previous experience with synthetic data generation or data augmentation techniques.
- Familiarity with AWS cloud computing platforms.
The salary information can be provided individually prior to the 1st interview
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