Large Language Models and Transformers have become the centerpiece of AI engineering interviews at top companies. OpenAI, Anthropic, Google DeepMind, and Meta all expect candidates to deeply understand everything from attention mechanisms to RLHF pipelines. These aren't just theoretical discussions anymore: you'll be asked to debug training runs, optimize inference systems, and make architectural decisions for billion-parameter models.
What makes these interviews particularly challenging is the expectation that you understand the full stack, from tokenization choices affecting multilingual performance to why your 70B model isn't scaling with batch size. A candidate might nail the math behind scaled dot-product attention but completely miss why pre-norm versus post-norm matters for large model stability. The questions jump between implementation details, scaling laws, and production trade-offs with little warning.
Here are the top 32 questions organized by the six core areas that define modern LLM engineering interviews.
Transformer Architecture
Transformer architecture questions separate candidates who've read papers from those who've debugged actual training runs. Interviewers focus heavily on design choices that only matter at scale: why RoPE outperforms sinusoidal encodings for long contexts, or why pre-norm becomes essential for deep networks. Most candidates can explain attention conceptually but stumble when asked to justify specific implementation decisions.
The key insight here is that every architectural choice in transformers exists to solve a concrete problem that emerges during training or inference. That $\sqrt{d_k}$ scaling factor isn't mathematical elegance, it's preventing your attention weights from saturating and killing gradients during backprop.
Tokenization & Embeddings
Tokenization seems basic until you're responsible for a production system serving 50 languages. Interviewers probe your understanding of how vocabulary size affects model capacity, why certain tokenizers fail on code or math, and how subword boundaries impact downstream performance. The most common failure is treating tokenization as a preprocessing step rather than a core modeling decision.
Smart candidates recognize that tokenization is where linguistic assumptions get baked into your model. A 32K vocabulary optimized for English will systematically undertrain on languages with different morphology, while aggressive subword splitting can destroy the semantic coherence that makes LLMs work.
Pre-training & Fine-tuning
Pre-training and fine-tuning questions reveal whether you understand the fundamental differences between learning general language representations and adapting them for specific tasks. Google and Meta interviewers particularly focus on RLHF implementation details and the failure modes of each training phase. Candidates often confuse the objectives and can't explain why masked language modeling works for BERT but fails for generative models.
The critical distinction is between learning to predict versus learning to behave. Pre-training teaches language structure through prediction, but RLHF teaches the model to optimize for human preferences, which introduces entirely different failure modes like reward hacking.
Scaling Laws & Training Dynamics
Questions about scaling laws and training dynamics test your ability to make resource allocation decisions with million-dollar compute budgets. DeepMind and OpenAI interviewers want to see that you can apply Chinchilla laws to real training decisions and diagnose when something's going wrong during a multi-week training run. The failure mode here is memorizing the scaling law equations without understanding their practical implications.
Understanding training dynamics means recognizing that loss curves tell stories about what your model is learning and when. That sudden loss drop after a plateau might be a phase transition where the model learns a new capability, or it could signal that your learning rate schedule needs adjustment.
Inference Optimization
Inference optimization separates candidates who understand transformers academically from those who've deployed them at scale. The challenges here are fundamentally different from training: memory bandwidth becomes the bottleneck, batch size scaling breaks down, and quantization introduces subtle accuracy degradations that only appear on complex tasks. Most candidates focus on theoretical speedups while missing the systems-level constraints that determine real performance.
The core insight is that inference is memory-bound, not compute-bound. Adding more GPUs won't help if you're already saturating memory bandwidth, and techniques like speculative decoding only work when your draft model is fast enough to overcome the overhead of running two models in parallel.
Alignment, Safety & Evaluation
Alignment and safety questions probe your understanding of the gap between what models optimize for and what humans actually want. Anthropic and OpenAI place heavy emphasis on reward modeling failure modes, benchmark contamination detection, and alternative alignment approaches. Candidates typically underestimate how easily reward models can be gamed and how difficult it is to specify human preferences mathematically.
The fundamental challenge in alignment is that optimizing for a proxy metric (like a reward model score) inevitably leads to exploiting the difference between the proxy and the true objective. Your model will find ways to score highly that humans never intended, which is why techniques like Constitutional AI try to make the optimization target more robust.
How to Prepare for LLMs & Transformers Interviews
Build a transformer from scratch in PyTorch
Don't just follow tutorials. Implement attention, positional encoding, and layer norm yourself, then debug why your gradients vanish or explode. This hands-on experience will make architectural trade-off questions much clearer during interviews.
Profile actual model inference on GPUs
Use tools like NVIDIA Nsight to understand where your model spends time during generation. Measure memory bandwidth utilization and see how batch size affects throughput. This practical experience is what separates good answers from textbook responses.
Train reward models on simple tasks
Implement a basic RLHF pipeline on a toy problem like sentiment control. Watch how your reward model fails when the policy finds unexpected ways to game the rewards. This will give you concrete examples of alignment failure modes to discuss.
Experiment with different tokenizers on diverse text
Compare how BPE, SentencePiece, and different vocabulary sizes handle code, math, and non-English text. Measure downstream task performance to understand how tokenization choices propagate through your entire system.
Read recent scaling law papers and replicate key plots
Don't just memorize the Chinchilla results. Understand the methodology and try to reproduce similar analysis on smaller models. This will help you apply scaling insights to novel scenarios during interviews.
