Engineering Intern – Gen AI for FP&A Platform
Drivetrain · United States
Experience: lead
Drivetrain is on a mission to empower businesses to make better decisions. Our financial planning & decision-making platform helps companies scale and achieve their targets predictably. Drivetrain is a remote-first company headquartered in the San Francisco Bay Area. Founded in 2021 by a couple of ex-Googlers, Drivetrain is a fast-growing company on a trajectory for success with backing from leading venture capital firms. Drivetrain provides a great culture for its employees to thrive in and be happy. 💜 Remote-friendly: Drivetrain brings together the best and the brightest, no matter where they are and provides them a great degree of autonomy. We trust our people. 🗣️ Open & transparent: We know that when our creators have access to all the information they need, their best work will emerge. 👏 Idea-friendly: We provide an environment to explore new ideas, to take risks, to make mistakes, and to learn, so you can succeed. Anyone in the company can come up with great ideas and become a catalyst for positive change. We let the best ideas win. 👥 Customer-centric: We follow a product-led growth strategy, continuously learning from our customers and collaborating to build the amazing software that Drivetrain is. About the Role We are seeking highly motivated Computer Science engineering interns passionate about Generative AI to join our team. You will work on real-world projects involving Retrieval-Augmented Generation (RAG), Agentic AI, and Large Language Models (LLMs) to enhance our FP&A (Financial Planning & Analysis) platform. This is a unique opportunity to gain hands-on experience at the intersection of AI and enterprise automation. Key Responsibilities Develop & Experiment: Build and prototype Gen AI solutions using RAG, agentic workflows, and LLMs for FP&A use cases. Collaborate: Work closely with product and engineering teams to integrate AI-driven features into the platform. Optimize: Apply strong computer science fundamentals to design efficient algorithms, data