AI Foundations - Software Engineer - Research Internship 2027

Join the pioneering AI Foundations Research team at IBM Research and contribute to shaping the future foundations of artificial intelligence. Our group of scientists, engineers, and designers is dedicated to conducting end-to-end research that delivers real-world AI impact through a rigorous, responsible, and open innovation framework. We are the team behind IBM's Granite open-source models, and our work spans multi-modal (vision, speech, language, code) and multi-lingual systems, data quality, data generation, novel model architectures, training recipes, governance and trustworthiness, and programming models for interacting with models. As an intern, you will explore cutting-edge research areas, including new algorithms for training, fine-tuning, reinforcement learning, and inference time scaling, alongside pioneering work in generative computing and generative programming for language, code, and other modalities, all within a collaborative environment that bridges fundamental science and transformative engineering. As an AI Software Engineer Intern, you will engage in the full research lifecycle to pioneer new advancements in artificial intelligence. Your role will involve identifying core challenges, designing novel prototype solutions, and validating them through rigorous experimentation. You will develop the robust, scalable infrastructure that powers cutting-edge AI research. Collaborating in small, expert-led teams, you will drive projects from concept to completion — transforming novel algorithms into high-performance, reusable code within modern, distributed frameworks, while contributing to IBM's open innovation initiatives. Foundational ML Knowledge: Familiarity with core concepts of modern AI, including Transformer architectures and Large Language Models (LLMs). Hands-on class or project experience is a plus. Programming Proficiency: Experience with Python for software development, scripting, or prototyping. Problem-Solving: Strong analytical and quantitative skills with an ability to break down complex problems. Communication: Ability to clearly explain technical concepts and work collaboratively within a team. Learning Mindset: A strong interest in AI research and a commitment to building high-quality, well-tested code. Technical Proficiency: Expertise in ML frameworks (PyTorch) and full-cycle development of algorithms and systems. Specialized Skills: Hands-on experience with generative AI (LLMs) and multimodal models. Communication: Ability to present complex research and build high-impact technical demonstrations.