Machine Learning Engineer Intern

Descrizione del lavoro:

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 2026
* 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
We acknowledge that many candidates may not meet every single role requirement listed above. If your experience looks a little different from our requirements but you believe that you can still bring value to the role, we'd love to see your application!​

Who We Are:

We're Criteo, the Commerce Intelligence Platform. Criteo helps businesses turn shopper signals into commerce outcomes while delivering more relevant experiences for shoppers. We use proprietary commerce intelligence and AI decisioning to drive relevance for shoppers and performance for businesses.

At Criteo, our culture is as unique as it is diverse. From our offices across the globe or from the comfort of home, our 3,600 Criteos collaborate together to build an open, impactful, and forward-thinking environment.

We foster a workplace where everyone is valued, and employment decisions are based solely on skills, qualifications, and business needs-never on non-job-related factors or legally protected characteristics.

What We Offer:

🏢 Ways of working - Our hybrid model blends home with in-office experiences, making space for both.
📈 Grow with us - Learning, mentorship & career development programs.
💪 Your wellbeing matters - Health benefits, wellness perks & mental health support.
🤝 A team that cares - Diverse, inclusive, and globally connected.
💸 Fair pay & perks - Attractive salary, with performance-based rewards and family-friendly policies, plus the potential for equity depending on role and level.

Additional benefits may vary depending on the country where you work and the nature of your employment with Criteo