AI/Machine Learning Engineer Intern

About the role

Join our team as an AI/Machine Learning Engineer Intern, where you'll have the opportunity to work on real-world AI-driven products and systems. You'll own an end-to-end project, develop backend services, integrate systems, build AI features, collaborate cross-functionally, shape AI quality, and engage in sprints. This is a great fit for motivated PhD students with strong backend fundamentals and a passion for AI. Key missions: Développer des services backend en Python pour alimenter des produits et des capacités partagées basés sur l'IA.. Construire des intégrations de services résilientes entre les systèmes internes, en gérant les modes de défaillance et les limites de taux.. Collaborer avec des chefs de produit, des chercheurs et des ingénieurs seniors pour transformer des cas d'utilisation de l'IA mal définis en systèmes concrets prêts pour la production. Profile: - We’re looking for people who are relentlessly curious and committed to continuous learning - Those who thrive here actively seek out new solutions, experiment thoughtfully, and apply what they learn to drive better, faster, smarter outcomes - As a motivated PhD student with strong backend fundamentals who is excited about building production AI systems. This role is a great fit for an aspiring software engineer who works comfortably in AI-driven problem spaces and wants to apply software engineering rigor to create LLM-backed products and platforms - We value curiosity, experimentation, and a commitment to continuous learning - Academic Background: Currently enrolled in a PhD program in Computer Science, Software Engineering, or a related quantitative field, graduating in 2027 - Python Proficiency: Excellent modern Python engineering skills, with the ability to write readable, performant, and testable code - Cybersecurity Interest: Curiosity about applying AI to cybersecurity or hands-on experience in the domain - Communication: Excellent communication skills and a collaborative approach to solving complex problems - System Design: Solid understanding of software engineering principles, including APIs, version control, and system architecture - AI Fundamentals: A strong background in AI/ML and experience with independent projects using LLMs, foundation models, or retrieval-augmented generation (RAG)