Q1
Walk me through your approach to implementing and evaluating a novel attention mechanism. What metrics would you use to determine if it outperforms standard transformers, and how would you account for computational trade-offs?
Why they ask this:* This assesses deep understanding of modern architecture design, rigorous evaluation methodology, and the ability to balance theoretical improvements with practical constraints—core competencies for senior researchers.
Q2
Describe a time when you had to debug a training instability in a large-scale model. What tools and techniques did you use to identify the root cause, and how did you validate your fix?
Why they ask this:* This tests hands-on experience with production-scale model development, systematic debugging skills, and the ability to troubleshoot complex, non-obvious problems in deep learning systems.
Q3
How would you design an experiment to measure whether your model exhibits a specific bias? Walk through your hypothesis, data strategy, statistical testing approach, and how you'd communicate findings to non-technical stakeholders.
Why they ask this:* This evaluates experimental rigor, understanding of fairness and interpretability in AI, and the ability to translate technical findings into actionable insights—increasingly critical for senior researchers.
Q4
Compare the trade-offs between fine-tuning a pre-trained foundation model versus training from scratch for a domain-specific task. What factors would influence your decision, and how would you quantify the cost-benefit analysis?