Intern Recommendation SystemsRakuten 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 has more than 250 million active users worldwide and provides some of the most popular Internet services in Japan today.
Terabytes of data are generated every day and once processed support the successful use-cases of providing recommendations to many services in Rakuten Ecosystem.
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.
About the Recommendation Team:
We are looking for a Recommendation Systems Intern to join the Rakuten Recommendation and Personalization team. You will join an international and cross-boarder team (Japan/Europe) providing recommendation and personalization for Rakuten Group services, and working on cutting-edge technologies in order to improve and personalize customers experience. In this position, you will interact with an international team of engineers and datascientists to provide the highest service quality to all our clients, and work with cross-functional teams within a dynamic and fast-paced environment.
Internship Description:
We are seeking a highly motivated and talented Recommendation Systems Intern to join our team. This internship offers a unique opportunity to contribute to real-world recommendation challenges and gain hands-on experience in a dynamic and innovative environment. The intern will work alongside experienced engineers and researchers on various projects aimed at improving the performance, explainability, and diversity of our recommendation systems.
Responsibilities (Potential Projects - Intern will focus on a subset):
- Self-Service Tooling (Engineering Focus):
Develop a self-service software package for R2P2 demo/sale activities, including a wizard-like UI, built-in platform essentials, API/UI for data ingestion, and a demo UI for recommendation results.
- Explainable Recommendations (AI/Research Focus):
Design a recommendation system that provides clear, user-friendly explanations for its suggestions, exploring techniques like attention mechanisms, counterfactual explanations, or interpretable embeddings.
- Smart Actions (AI/Research Focus):
Research, define, and prototype chat-free applications of LLMs to enhance recommendation systems, focusing on minimal interaction (e.g., button clicks).
- Evaluation (Engineering/Research Focus):
Research, define, and prototype processes and techniques for evaluating highly-personalized and contextual recommendation results, implementing them into automated tools.
- Feeds Diversity (AI/Research Focus):
Research, define, and prototype methods to enhance diversity in recommendation feeds, potentially leveraging Determinantal Point Processes (DPPs) and other techniques.
Other tasks as assigned, related to recommendation system development, evaluation, and improvement.
Qualifications:
- Currently pursuing a Bachelor's or Master's degree in Computer Science, Data Science, Statistics, or a related field.
- Strong programming skills in Python.
- Familiarity with machine learning concepts and techniques.
- Excellent problem-solving and communication skills.
- Ability to work independently and as part of a team.
- A passion for recommendation systems and personalized experiences.
Bonus Points (Skills that would be beneficial for specific projects):
- Experience with deep learning frameworks (e.g., TensorFlow, PyTorch).
- Experience with Large Language Models (LLMs).
- Knowledge of explainable AI (XAI) techniques.
- Experience with UI development (e.g., React, Angular, Vue.js).
- Experience with containerization technologies (e.g., Docker).
- Strong foundation in statistics.
- Familiarity with UI/UX principles for evaluation tools.
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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