AI Engineering Intern

Job description

AI engineers at QuantCo build cutting-edge systems that bridge the gap between advances in frontier model technology and real-world impact in the world's foremost companies. They design, build, and implement applied AI systems that improve the quality of human decisions and unlock major efficiencies. AI engineers work with data scientists to solve complex problems in inference optimization, scalable systems design, and human-computer interaction. Team members may work in forward-deployed roles, collaborating directly with clients to discover, rapidly iterate on and deploy AI solutions, as well as in centralized roles that abstract successful solutions into scalable assets. Our team combines technical deep learning and engineering expertise with an entrepreneurial mindset driven by rapid experimentation, first-principles thinking, and a passion for tackling complex real-world problems to deliver lasting impact. **Description** You will design, test, and deploy AI systems that are embedded at scale. Operating at the intersection of machine learning and engineering, you will leverage cutting-edge models to build robust, production-ready AI applications. You will lead the design of data and inference pipelines, orchestrate the integration of LLM-based components such as retrieval-augmented generation (RAG), and balance engineering constraints with business priorities. From system design to deployment, you will own all stages of product iteration and development, building solutions that seamlessly integrate into enterprise settings, designed to change the way our clients work with AI. ### Requirements - BSc, MSc or PhD in Computer Science, Engineering, Physics, or a related technical or STEM field from a leading university. - Experience working with relevant models and/ or architectures within the following fields: Computer Vision, Natural Language Processing, or Graph Neural Networks. - Experience programming in Python. - Familiarity with the frontier labs APIs (Gemini, OpenAI, Anthropic), and their advanced features like structured outputs and tool calling. - Understanding of LLM inference considerations e.g. input vs. output tokens, prompt caching. - Strong computer science fundamentals in areas such as data structures, cloud infrastructure, distributed systems, machine learning, and high-performance computing. - Experience and willingness to work through the challenges associated with large, unstructured, real-world data sets.