Daniel Zhang
Q: How did you spend your summer? This summer, I worked as an AI/Software Engineering Intern at a biotechnology company called Protillion Bioscience, where I helped develop and improve the infrastructure supporting research software. My work focused on making software more reliable, reproducible, and easier to run across different computing environments. I worked on containerizing software tools, automating testing and deployment, and troubleshooting technical challenges along the way. As an AI major, I was especially interested in seeing how reliable software infrastructure can support computational and AI-driven research. The experience gave me a broader perspective on the engineering systems that make advanced software and AI applications possible.
Q: What did you learn? I learned that real-world engineering is often less about finding one “correct” answer and more about understanding a problem, considering different approaches, and making thoughtful decisions based on practical constraints. I also learned how important it is to understand existing systems before trying to improve them. Working with an established codebase taught me how to navigate unfamiliar code, ask better questions, and build on the work of other engineers. As an AI student, this was particularly valuable because developing useful AI systems requires more than designing models—it also requires strong software engineering, reliable infrastructure, and the ability to work effectively with complex systems.
Q: Were you able to apply any knowledge or skills you learned in the classroom to your summer internship? Yes. My coursework as an AI major at UC San Diego provided a strong foundation that I was able to apply throughout the internship. CSE 29, Systems Programming and Software Tools, was particularly relevant because it introduced me to systems-level concepts such as processes, memory, and how software interacts with the operating system. The course also gave me practical experience working in a UNIX command-line environment. I was able to apply those concepts directly while working with containers, automation, testing, and software infrastructure. My AI coursework also helped me think about software from the perspective of building systems that can eventually support data-driven and intelligent applications.
Q: Did this experience influence or confirm your future goals at all? The experience reinforced my interest in pursuing a career at the intersection of AI and software engineering. Studying AI has taught me about the algorithms and concepts behind intelligent systems, while this internship showed me the engineering infrastructure required to make software reliable and useful in practice. I came away with a greater appreciation for how closely AI and software engineering are connected. In the future, I hope to work on AI systems that are not only innovative, but also reliable, scalable, and useful for solving real-world problems.
Q: Anything else you’d like to share? One of the biggest lessons I took away from the summer is that engineering is often about making good decisions without having all the answers. There were times when I had to work through unfamiliar problems, evaluate multiple possibilities, and determine the best path forward. Learning to be comfortable with that uncertainty and to support my decisions with careful reasoning was one of the most valuable skills I developed.
As an AI major, I also came away with a stronger understanding that building impactful AI is not just about the models themselves. It requires strong foundations in software, systems, testing, and infrastructure. This experience helped me see how the different areas I am learning about can come together to build technology that can have a meaningful impact.