Applied AI Research Intern - R&D Internship – 2027 - YORKTOWN HEIGHTS

Join the pioneering Software Innovation Lab team at IBM Software and contribute to shaping the future foundations of artificial intelligence and data. Our group of applied scientists and software developers 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 R&D team that is the innovation engine of IBM Software, shaping the technologies that IBM customers rely on – today and in the future. As an AI Research Intern, you will explore cutting-edge research areas, with a focus on AI models and approaches for real-time data and context backbone for agents and stream-native AI. Internship topics include event world models, real-time context optimization for agents and models, semantic stream processing for stored and streaming data. You will explore efficiency, accuracy and scalability challenges, test new theories, design experiments to validate novel computational models, all within a collaborative environment that bridges fundamental science and transformative engineering. You will help develop Event World Models, a new class of AI systems that learn, reason, predict, and plan from continuously evolving event streams and enterprise data. You will contribute across the research lifecycle, from formulating research questions and designing novel algorithms to building prototypes and evaluating them through rigorous experimentation. Research topics include event-driven world modeling, temporal and causal reasoning, symbolic and foundation models for structured and streaming data, semantic action discovery, AI agents, and stream-native AI systems capable of real-time prediction, what-if analysis, and decision-making. Working in a small, mentored research team, you will contribute to publications, patents, open-source artifacts, and technical demonstrations that advance the state of the art in AI and intelligent data systems. Program Dates: Internship start dates vary based on your academic calendar. • Semester System: May 24, 2027 – August 13, 2027 • Quarter System: June 14, 2027 – September 3, 2027 Candidates must be available to participate for the full duration of the internship corresponding to their academic schedule. Location Flexibility: By applying to this requisition, you acknowledge and agree to be considered for any of the listed locations associated with this position and are willing to work at the location where you are ultimately assigned. AI/ML Research Experience: Knowledge of machine learning, LLMs, AI Planning, transformer models, or generative AI, supported by strong theoretical foundations. Programming & Prototyping: Proficiency in Python and ML frameworks (e.g., PyTorch) for research and rapid experimentation. Analytical & Experimental Skills: Ability to formulate hypotheses, design experiments, analyze results, and solve complex problems involving sequential or structured data. Research Communication: Strong written and verbal communication skills, demonstrated through publications, academic projects, or open-source contributions. Relevant Research Areas: Experience in world models, sequence modeling, reinforcement learning, AI planning, knowledge representation, or causal reasoning. Foundation Models & Structured Data: Experience applying LLMs or foundation models to structured, event, or enterprise data. Streaming & Event-Driven Systems: Familiarity with event streams, Kafka, Flink, Spark Streaming, or related technologies. Research Publications: Publications or submissions to leading AI, ML, data management, or systems conferences (e.g., NeurIPS, ICLR, AAAI, ICAPS, KDD, VLDB, SIGMOD). United States Research Hybrid Internship Yorktown Heights, US Yorktown Heights (0147) International Business Machines Corporation Apache Kafka Artificial Intelligence Communication Conferences Data Management Design Machine Learning Modeling Planning Computer Programming Publications Python Research Research and Development Transformer Architecture Prototyping PyTorch Structured Data Apache Flink Artificial Intelligence Research Large Language Model Reinforcement Learning Verbal Communication Spark Streaming Knowledge Representation Knowledge Discovery in Databases