Question 1

Explain the transformer architecture. What problem did attention mechanisms solve that RNNs could not?

Accepted Answer

RNNs process sequences sequentially — gradient vanishes over long distances. Attention computes pairwise token relationships in parallel, capturing long-range dependencies. Transformers scale better on hardware (parallelism) and with data.

Question 2

What is the difference between pre-training, fine-tuning, and RLHF? Why is each stage needed?

Accepted Answer

Pre-training: learn world knowledge from vast text. Fine-tuning: adapt to task format and domain. RLHF: align model behavior with human preferences (helpfulness, harmlessness). Each stage builds on the prior; RLHF is what makes models usable, not just accurate.

Question 3

How do scaling laws inform decisions about model size, dataset size, and compute budget?

Accepted Answer

Chinchilla scaling laws: for a given compute budget, the optimal model is smaller than previously assumed, trained on more tokens. Implies data collection is as important as model architecture decisions. Guides the build vs fine-tune decision.

Question 4

What is catastrophic forgetting in neural networks, and what techniques mitigate it?

Accepted Answer

When training on new tasks, the model overwrites weights that encoded prior tasks. Mitigations: elastic weight consolidation (EWC), replay buffers (mix new and old data), progressive neural networks, and LoRA for fine-tuning (modifies few parameters).

Question 5

Tell me about a research project where your initial hypothesis was completely wrong. How did you adapt?

Accepted Answer

Shows scientific integrity and adaptability. Cover how quickly you detected the failure, how you reframed the research question, and what you published or learned even from the negative result.

Question 6

Describe the hardest ablation study you've designed. What made it difficult to isolate the variable you cared about?

Accepted Answer

Shows experimental rigor: confounding variables, computational cost of running ablations at scale, and how you made design decisions about what to control vs accept as noise.

Question 7

Tell me about a time you had to communicate speculative research findings to a team that needed production-ready answers.

Accepted Answer

Show the tension between research uncertainty and engineering timelines. Cover how you represented confidence intervals, what you recommended vs deferred, and how you built a path from research signal to engineering experiment.

Question 8

Design a research infrastructure for a team of 20 researchers running hundreds of GPU experiments simultaneously.

Accepted Answer

Cover experiment tracking (Weights & Biases, MLflow), GPU cluster scheduling (SLURM, Ray), checkpoint management, reproducibility requirements (code + config versioning), dataset registry, and collaboration on shared baselines.

Question 9

How would you design an evaluation framework for a new language model benchmark to minimize dataset contamination?

Accepted Answer

Cover held-out test sets (no overlap with pre-training corpora), n-gram decontamination checks, time-cutoff splits, diverse task distribution, and dynamic evaluation (new questions generated per evaluation to prevent memorization).

Question 10

Design a human preference data collection pipeline for RLHF training at scale.

Accepted Answer

Cover annotation task design (pairwise preference vs scalar rating), annotator qualification and agreement measurement (inter-annotator agreement), quality control (gold examples, adversarial checks), and handling annotator bias at scale.

AI Researcher Interview Questions 2026

Technical

Behavioral

System Design