Q1
Walk me through your approach to designing an experiment to validate whether a novel neural architecture improvement actually generalizes better than the baseline, rather than just overfitting to your specific dataset and evaluation metrics.
Why they ask this:* They want to assess your understanding of rigorous experimental design, statistical significance, generalization testing, and your ability to avoid common pitfalls in AI research validation.
Q2
Describe your experience with distributed training frameworks (PyTorch DDP, TensorFlow distributed, or similar). How have you debugged convergence issues when scaling a model across multiple GPUs or TPUs?
Why they ask this:* They need to know if you can handle the practical complexities of training large models at scale, troubleshoot communication bottlenecks, and optimize training efficiency in production settings.
Q3
Tell me about a time you had to implement or adapt a paper's methodology from scratch. What ambiguities did you encounter in the paper, and how did you resolve them?
Why they ask this:* They're evaluating your ability to translate research literature into working code, your attention to detail, your problem-solving when specifications are incomplete, and your research independence.
Q4
How do you approach hyperparameter tuning for a complex model, and what's your philosophy on balancing computational budget against search space exploration versus exploitation?