Q1
Describe your experience with transformer architectures and attention mechanisms. How have you optimized these models for production, and what trade-offs did you consider between model size, latency, and accuracy?
Why they ask this:* Transformers are foundational to modern AI. They want to assess your depth of understanding in a core architecture, your ability to optimize for real-world constraints, and your practical experience moving research into production.
Q2
Walk us through your approach to designing and running large-scale experiments. What frameworks, tools, or methodologies do you use to track hyperparameters, manage computational resources, and ensure reproducibility across multiple GPUs or TPUs?
Why they ask this:* Senior researchers must manage complex experimental pipelines efficiently. This reveals your systematic thinking, familiarity with MLOps tools, and ability to scale research rigorously.
Q3
Explain a novel loss function or training objective you've designed or implemented. What problem did it solve, how did you validate its effectiveness, and what were the computational or convergence implications?
Why they ask this:* This tests your ability to innovate beyond existing methods and demonstrates deep technical knowledge of optimization, loss landscapes, and empirical validation—core to research advancement.
Q4
Describe your experience with distributed training across multiple nodes. What synchronization strategies, gradient aggregation techniques, or communication protocols have you worked with, and how did you handle convergence issues?