As an AI Engineer Intern, you will work on advanced Computer Vision and Deep Learning topics

You will join a squad of Data Scientists and Engineers to upgrade our high-volume analysis pipeline. Your main mission will be to integrate next-generation multimodal models into our production environment to process massive datasets daily.

Your Missions:

• Design Hybrid Pipelines: Architect a tiered system that combines high-speed detection algorithms with deep semantic understanding models to verify complex inputs.

• Optimize for Scale: Experiment with advanced inference techniques and parallel processing strategies to run large-scale models cost-effectively.

• Benchmark & Validate: Rigorously test new model performance against existing baselines and human labels to ensure production-grade accuracy.

• Deploy: Assist in containerizing your solutions for seamless integration into our cloud and GPU infrastructure.

Work environment perks:

• Young startup working on an impactful and huge challenge backed by great VC funds!

• Cool offices in the center of Paris (9ème arrondissement).

• Flexible remote policy.

• Alan, Swile (Meal Vouchers).

• Fun company traditions: offsites, weekly games together…

• Real mentorship: You won’t just be running experiments; you will learn how to ship AI to production.

Our ideal candidate

• Final-year or gap year student (Master 2 or Engineering School) in Computer Science, Data Science, or Machine Learning.

• Builder mindset: You love coding and seeing your models actually work in a software stack, not just in a notebook to bring value.

• Autonomy and curiosity: You are passionate about the rapid evolution of Deep Learning architectures and love testing new open-source models as soon as they are released.

• Interest for environmental issues and the circular economy.

Expected Skills

• Strong Python skills: You write clean, modular, and production-ready code.

• Deep Learning: Proficiency with major industry frameworks (e.g., PyTorch, TensorFlow) and open-source ecosystems.

• Model Architecture: Understanding of how modern large-scale models process text and images (attention mechanisms, embeddings).

• Computer Vision: Solid grasp of fundamental image processing concepts.

• Engineering Basics: Comfort with containerization, version control, and Linux environments.

1. Introduction call (30m) with the Data Science Manager.

2. Technical interview (1h) with your future mentor covering python proficiency and core concepts in vision and machine Learning.

3. Meet the CTO at the office to discuss the product vision!