Full Stack Engineer Intern

Join our team as a Machine Learning Engineer Intern, where you'll help build an internal AI assistant that allows employees to access company knowledge through natural-language questions. You'll design and implement a secure Retrieval-Augmented Generation (RAG) pipeline, develop automated data pipelines, fine-tune open-source large language models, and generate actionable insights. Enjoy unlimited PTO, flexible working arrangements, health benefits, equity options, professional development opportunities, and daily catered lunches.

• Contribuer à la construction d'un assistant AI interne permettant aux employés d'accéder instantanément aux connaissances de l'entreprise.
• Développer et déployer un chatbot AI interne permettant aux employés de poser des questions sur les résultats des tests en utilisant un langage naturel.
• Concevoir et construire un pipeline de génération augmentée par récupération (RAG) sécurisé pour extraire des données contextuelles à partir de sources internes.
• Open-Source LLM Experience: Hands-on experience deploying, fine-tuning, or quantizing open-source models (e.g., Qwen, LLaMA, Mistral) using frameworks like Hugging Face or vLLM
• Autonomous Vehicle Domain Knowledge: Familiarity with autonomous driving data formats (e.g., ROS bags), simulation environments, or road testing metrics
• Vector & Relational Databases: Experience working with vector databases (e.g., Milvus, Chroma, FAISS) as well as querying traditional SQL/NoSQL databases
• Data Engineering Fundamentals: Experience building data extraction, transformation, and loading (ETL) pipelines, as well as handling both structured and unstructured data
• Familiarity with RAG: Core understanding of Retrieval-Augmented Generation workflows, text chunking, and vector embeddings
• Python Programming: Strong proficiency in Python for machine learning workflows, scripting, and backend system integration
• Machine Learning & NLP: Solid understanding of Large Language Models (LLMs), natural language processing, and prompt engineering
• Data Security & Privacy: An understanding of best practices for deploying ML models locally or within secure, internally-hosted environments
• Chatbot Frameworks: Experience with LLM orchestration frameworks such as LangChain or LlamaIndex