Machine Learning Engineer Intern

What You'll Do:

As a Machine Learning Intern in the Onsite Recommendation Models team, you’ll work closely with experienced ML engineers to tackle a real, ambitious production challenge: building a unified multi‑task recommendation model.

From day one, you’ll be supported by a mentor and fully integrated into the team. You’ll be encouraged to ask questions, share ideas, and actively participate in technical discussions, workshops, and knowledge‑sharing sessions.

Your work will have real impact, powering recommendation systems used by millions of users.

During the internship, you will:

• Dive deep into our current recommendation systems, such as Search‑to‑Product, Product Similarity, and Product Complementarity
• Explore state‑of‑the‑art multi‑task learning approaches for recommendation systems
• Extend and improve an existing two‑tower deep learning model to support multiple recommendation tasks
• Design clear experimentation plans: model architectures, training objective, evaluation metrics and protocols
• Implement and train deep learning models using Python and PyTorch
• Run experiments, analyze results, and compare performance across tasks
• Contribute clean, scalable, production‑ready code following best practices
• Document your findings and share insights with the team at the end of the internship

By the end of the internship, you’ll have worked on a challenging ML problem in a real production environment, gaining hands‑on experience with large‑scale data and modern recommendation systems.

Who You Are:

You’re a curious and motivated student who enjoys solving complex problems and turning ideas into working models.

You might be a great fit if you:

• Are currently in the final year of a BSc or MSc in a quantitative field (Computer Science, Engineering, Mathematics, Statistics, or related)
• Have a strong foundation in machine learning and mathematics
• Are comfortable coding in Python
• Have hands‑on experience or coursework in Deep Learning, ideally with PyTorch
• Enjoy working with data and experimenting with models
• Can communicate clearly in English, both written and spoken
• Are eager to learn, iterate, and take ownership of a technical project
• Available to start an on‑site internship in Paris from June
• Nice to have (but not required):
• Familiarity with recommendation systems
• Experience with large‑scale or distributed training (e.g. GPUs, Ray)
• Exposure to Spark / PySpark