More companies in the U.S. are starting to explore large language models—not just big tech firms focused on cutting-edge AI, but also smaller startups and mid-sized businesses. These companies are beginning to look for people who understand how to work with this technology. For international students hoping to enter this field, competition can be intense. That’s why having a clear, focused resume and a few solid projects is more important than ever.
A resume doesn’t need to be overly polished or filled with buzzwords. What matters most is that it reflects real experience and thoughtful work. Most hiring teams want to see that you have a grasp of machine learning, deep learning, and especially the currently popular large language model architectures. It helps if you're familiar with models like Transformers or GPT. If you’ve worked on anything like text generation, Q&A systems, or fine-tuned a pre-trained model, take the time to explain what you did, why you did it, and what results you saw. Details matter more than broad claims.
Working with data is just as critical. Many projects begin with hours of cleaning and organizing data before modeling even starts. Showing that you’ve handled data preparation, model development, and possibly even deployment gives your work credibility. People want to know you can carry a project from start to finish, not just run code that someone else wrote.
As for projects, they don’t have to be groundbreaking. Even a small chatbot or a domain-specific fine-tuning experiment can make a difference, as long as it’s real and well-executed. Make sure you can talk about the goals, what tools you used, and what you learned. If you’ve worked on team projects—whether through coursework, research, or internships—that’s a plus. U.S. companies often value teamwork and communication just as much as technical skill.
What matters most is that your resume tells a true story. Instead of cramming in every tool or language you’ve ever touched, focus on 1–2 projects that show you’ve applied your knowledge thoughtfully. If your work ties into a real use case, like automating support tasks or processing healthcare records, that shows you understand how these tools fit into the bigger picture.
The job hunt doesn’t pay off overnight. It takes time, consistency, and reflection. But if you keep building, learning, and sharing your work, you’ll eventually find your way in.
Release time:2025-05-16
