The Qualcomm Research Intern interview process was structured in two stages, and overall it felt like a clear progression from validating ML/DL fundamentals to assessing research fit and long-term alignment. Round 1 was a technical screen with a Senior Engineer. The focus was on core machine learning and deep learning fundamentals—making sure I could explain key concepts clearly and reason through them rather than just recite definitions. The questions covered typical ML/DL basics such as training dynamics, loss functions, optimization, overfitting vs. underfitting, and general architectural considerations. The conversation was fairly interactive, with follow-up questions based on my answers, but it didn’t feel overly difficult or adversarial. The tone was calm and practical, and it seemed primarily designed to confirm that I had a solid foundation and could communicate technical ideas accurately. Round 2 was with the Hiring Manager and was more research-oriented. This interview centered on my past research experience and what I would like to work on going forward. I was asked to walk through the research I’ve done so far—what problem I was trying to solve, why it mattered, what approach I chose, how I designed experiments, and what I learned from the results. The discussion emphasized my reasoning process and ownership: what decisions I made, how I handled unexpected outcomes, and how I iterated based on findings. The Hiring Manager also asked about the types of research directions I’m most interested in pursuing next and how those interests align with the team’s work. This portion felt like a fit and alignment conversation: whether I have a coherent research narrative, whether my interests match the group’s priorities, and whether I can articulate realistic goals for an internship.