Technical practice
Reproducing Research Papers
What to coverExplain your process for reproducing a state-of-the-art model from a research paper. Focus on how you handle missing implementation details, verify baseline performance, and ensure your results are statistically comparable to the original findings.
Ablation Study Design
What to coverDescribe how you design an ablation study to isolate the impact of specific components in a novel architecture. Discuss how you balance computational cost with the need to rigorously prove which modules contribute to performance gains.
Fine-tuning Techniques (SFT and RL)
What to coverCompare Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) approaches for aligning LLMs. Discuss the trade-offs between these methods regarding data quality requirements, training stability, and the risk of reward hacking.
Scientific AI and Multimodal Integration
What to coverHow would you approach integrating multimodal data (e.g., chemical structures and text descriptions) into a reasoning system for materials discovery? Address the challenges of aligning different data modalities and maintaining scientific accuracy.
Distributed Training and Efficiency
What to coverWhat strategies do you use to optimize model training across distributed GPU infrastructure? Discuss techniques for managing memory bottlenecks, data parallelism, and efficient experiment tracking to ensure reproducibility.
Mathematical Foundations in Deep Learning
What to coverHow do probability and statistics inform your approach to model evaluation and uncertainty estimation in scientific AI? Explain how you interpret confidence intervals or variance in experimental results when dealing with small or noisy datasets.
Building Benchmarking Pipelines
What to coverDescribe the architecture of a reliable benchmarking pipeline you have built. Focus on how you ensure data integrity, automate evaluation metrics, and maintain version control for both code and datasets.
Implementing Algorithms from Scratch
What to coverShare an experience where you implemented a complex algorithm from scratch rather than using a library. Explain the challenges you faced in debugging the mathematical implementation and how you validated its correctness.
Behavioural practice
Questioning Existing Approaches
What to coverDescribe a situation where you identified a flaw or limitation in an established research method. Explain how you communicated your findings to your team and what steps you took to propose or implement a better alternative.
Handling Research Dead-ends
What to coverTell me about a time when a hypothesis you formed did not yield the expected results after extensive experimentation. How did you analyze the failure, and what did you learn that influenced your next research direction?
Bridging Theory and Implementation
What to coverProvide an example of how you translated a theoretical concept from a paper into a practical, working prototype. How did you manage the trade-offs between theoretical elegance and the constraints of real-world manufacturing applications?
Collaborative Research Documentation
What to coverHow do you ensure your research code and documentation are accessible and reproducible for others? Describe a time when your documentation practices helped a teammate or stakeholder understand your experimental process.