Intern Recommendation Systems

Rakuten; Paris; Sur place; EUR 60 000 - 80 000; Plein temps

Résumé du poste

Rakuten is seeking a highly motivated Recommendation Systems Intern to work on diverse projects aimed at improving recommendation systems' performance and explainability. This internship offers hands-on experience in a dynamic environment, collaborating with experienced engineers and researchers. Ideal candidates are pursuing a degree in relevant fields with strong programming skills, particularly in Python.

Qualifications

• Currently pursuing a Bachelor's or Master's degree.
• Strong programming skills in Python are required.
• Familiarity with machine learning concepts is essential.

Responsabilités

• Develop self-service software packages for demo/sale activities.
• Design recommendation systems focusing on explainability.
• Research and prototype methods for enhancing recommendation diversity.

Connaissances

Programming Skills

Problem-solving

Communication Skills

Machine Learning Concepts

Teamwork

Passion for Recommendation Systems

Formation

Bachelor's or Master's degree in Computer Science, Data Science, Statistics, or a related field

Outils

Python

TensorFlow

PyTorch

Docker

UI/UX Principles

Description du poste

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) :

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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English (Overall - 3 - Advanced)