AI Software Engineer Intern

At IBM Software, we transform client challenges into solutions. Building the world's leading AI-powered, cloud-native products that shape the future of business and society. Our legacy of innovation creates endless opportunities for IBMers to learn, grow, and make an impact on a global scale. Working in Software means joining a team fueled by curiosity and collaboration. You'll work with diverse technologies, partners, and industries to design, develop, and deliver solutions that power digital transformation. With a culture that values innovation, growth, and continuous learning, IBM Software places you at the heart of IBM's product and technology landscape. Here, you'l have the tools and opportunities to advance your career while creating software that changes the world. Build AI for the systems the world relies on. At IBM Z, our technology supports the transactions and data behind banking, payments, and global commerce. We are bringing AI into that foundation to help developers build faster, simplify complex operations, and solve problems where reliability, security, and performance matter. As an AI Software Engineer Intern, you will work alongside experienced engineers to turn advances in large language models into useful software. Depending on your team, you could build an agent that helps diagnose system issues, connect AI to enterprise data and tools, or create a simpler way for developers to work with complex systems. Bring your ideas, experiment, and help take a feature from prototype through testing and refinement. What You’ll Do: Design, build, and test AI-powered features that address real developer and client needs, with guidance from experienced engineers. Integrate LLMs, APIs, and enterprise data into applications, exploring agent workflows, tool calling, and retrieval where they fit the problem. Write clear specifications and effective prompts, use AI development tools thoughtfully, and review and test the code they produce. Evaluate AI behavior with realistic test cases, measure quality and performance, and improve how applications handle incorrect outputs and failures. Debug across model interactions, application code, APIs, and underlying systems to understand root causes and build reliable solutions. Collaborate through Git, code reviews, and technical discussions; document your work and iterate on feedback. 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. Strong software engineering fundamentals, including data structures, algorithms, software design, and testing. Proficiency in Python and experience building applications or services that integrate APIs. Hands-on experience building an application or working prototype with LLMs. You can explain what you built, how you integrated the model, and how you tested its behavior. Experience may come from personal, academic, open-source, or internship projects. A systematic debugging mindset: you can investigate failures, test assumptions, and reason about how application code, data, models, and underlying systems interact. Ability to translate a problem into clear requirements, prompts, and testable acceptance criteria, and critically evaluate AI-generated code and responses. Familiarity with Git, clear technical communication, and the ability to learn quickly and collaborate on a team. Experience in one or more of the following areas is a plus. You do not need experience with every technology listed. AI agents and tool or function calling; retrieval-augmented generation (RAG), embeddings, vector databases, or Model Context Protocol (MCP). Independent AI projects, open-source contributions, or experiments beyond assigned coursework that demonstrate initiative and thoughtful engineering decisions. Additional programming experience in JavaScript/TypeScript or Go, and familiarity with distributed systems or microservices. Operating system concepts such as concurrency, multithreading, and memory management; relational databases and SQL. Cloud and container technologies such as Docker, Kubernetes, or OpenShift; observability, search, or real-time data processing tools such as OpenSearch or Kafka. AI evaluation, anomaly detection, or experience measuring and improving model accuracy, latency, and reliability. We are especially interested in what you have built and learned. Where available, include a project link, repository, demo, or a brief description of your contribution and the technical challenges you worked through.