Internship: GenAI-Assisted Software Modernization

Job description

Work Schedule Standard (Mon-Fri) Environmental Conditions Office Job Description Help us use generative AI to understand and modernize a life sciences application. How can AI help engineers understand an established software application and build its next generation? In this internship, you’ll explore that question through a practical software engineering project. You’ll investigate an existing life sciences application, organize knowledge about how it works, and use that knowledge to help AI agents support software modernization. You’ll then apply your approach to a focused feature and contribute to a working proof of concept. The project You’ll explore how to bring together information from source code, documentation, tests, and other engineering materials. Using techniques such as static analysis, knowledge graphs, retrieval-augmented generation (RAG), and generative AI, you’ll help create a structured view of the application and its behavior. You’ll use this knowledge to help AI agents answer questions, identify the impact of changes, and support development. To demonstrate the approach, you’ll help modernize a session setup feature, following it from the user interface through configuration, application logic, validation, and data storage. Your profile You are a bachelor’s graduate, a master’s student, or a master’s graduate in computer science, software engineering, artificial intelligence, or a related field. You have: A solid foundation in programming and software engineering; experience with C# is a plus. An understanding of software architecture and object-oriented design. Familiarity with version control, debugging, and testing. An interest in generative AI and its use in software development. Curiosity and problem-solving skills to explore an unfamiliar codebase. Good communication skills and the ability to work both independently and with others. What you’ll gain You’ll gain hands-on experience applying AI to a real software engineering challenge. You’ll learn how to investigate a complex life sciences application and contribute to a proof of concept for modernizing it. Appy today!