Applied scientist intern

Job Description
Rakuten Group

Rakuten, founded in 1997, is a Global Innovation Company based in Japan. With over 70 diverse businesses spanning e-commerce, digital content, fintech, and communications, and 32,000 employees, we serve 1.6 billion members worldwide. Our mission is to empower people and society through innovation and entrepreneurship.

Rakuten Tech in Europe

Rakuten Tech in Europe, a part of the Rakuten Group's Global Innovation Hub, serves as the regional hub for the European-based members of the Technology Division. We provide and optimize global platforms to support businesses within the Rakuten Ecosystem, tailoring them to specific use cases in Europe and beyond.

With over 130 members across 7 countries and 12 offices, our presence spans France (Paris), Spain (Barcelona), UK (Belfast and London), Estonia (Tallinn), and Germany (Berlin). Our diverse team is formed of more than 20 nationalities and collaborates with all members of the Technology Divisions on a regular basis.

Team's presentation
Rakuten Institute of Technology: The incubation center of AI research and technologies that fuel Rakuten's innovation.

Rakuten Institute of Technology (RIT), the core AI research wing of Rakuten, is spread across six geographical locations including Tokyo, Singapore, Boston, San Mateo, Bengaluru, and Paris. This division consists of AI research scientists with specialization in machine learning, deep learning, natural language processing, computer vision, knowledge discovery and data mining.

Position's Description
As an intern, you will play a key role in exploring the potential of LLMs in the context of item recommendation. Your responsibilities will include data gathering and preparation, evaluating the recommendation capabilities of LLMs under zero-shot settings, as well as finetuning and pretraining LLMs for various recommendation tasks.

In More Details, You Are Expected To

• Perform comprehensive literature review on LLMs and their applications in recommendation systems.
• Gather and preprocess data from various sources, ensuring its adequacy for recommendation tasks.
• Investigate the effectiveness of LLMs in recommendation scenarios where no direct training data is available.
• Implement evaluation pipelines and conduct thorough experiments to assess the recommendation capabilities of LLMs.
• Implement/refine finetuning and pretraining techniques for LLMs, specifically focusing on recommendation tasks.
• Collaborate closely with the team to analyze and interpret experimental results.
• Document your findings and contribute to prototyping LLMs-based recommendation solutions.

Profile

• Currently enrolled in a computer science Master 2 program (or equivalent) specialized in machine learning.
• Strong programming skills, preferably in Python, and experience with deep learning frameworks such as PyTorch.
• Strong machine learning background and familiarity with recommendation systems, natural language processing (NLP).
• Solid understanding of transformer architectures, especially in the context of LLMs.
• Proficiency in leveraging existing datasets and data preprocessing techniques for machine learning tasks.
• Excellent analytical and problem-solving skills, as well as attention to detail.
• Full proficiency in English and the ability to work independently and collaboratively as part of a team.

Benefits

• Compensation ranges : 1 100 to 1 320 €
• Lunch vouchers
• Office in Sentier neighbourhood
• Being part of multicultural teams with more than 20 nationalities
• Gain practical experience in cutting-edge research and technologies.
• Work in a collaborative and supportive environment with experienced scientists and engineers.
• Access to relevant resources, datasets, and computational resources.
• Potential authorship on research publications and technical reports.

As an employer, Rakuten Tech in Europe is committed to developing an inclusive working environment. Access to employment is open to all, regardless of gender, age, disability, ethnicity, religion, sexual orientation, or social status.

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Languages
English (Overall - 3 - Advanced)