Abridge Applied AI Engineer interview questions

Almost everyone can quote softmax(QKᵀ/√d_k)V. The interviewer wants the variance argument and the exact training failure the scale prevents. Here is the answer that separates memorization from understanding.

The single most-asked applied GenAI question, and the one most candidates answer as a definition dump. The interviewer wants a decision framework with a default and the conditions that flip it. Here is the call that scores.

MoE is why some frontier models have huge parameter counts but serve cheaply. The signal is the sparse-activation idea, the routing mechanism, and the operational cost (memory, load balancing) that the FLOP savings quietly hide.

Tokenization looks like plumbing until it explains your bill, your latency, and why the model fumbles numbers and rare languages. The signal is knowing how subword tokenizers work and the consequences that fall out of them.

What each knob does to the next-token distribution, why top-p adapts to confidence where top-k does not, and why factual and creative tasks want opposites.

Alignment is now table stakes even outside 'safety' roles. The interviewer wants the three-stage RLHF pipeline and a crisp account of why DPO dropped the reward model entirely. Here is the answer with the mechanism, not the buzzwords.

The trap is jumping straight to 'add more GPUs.' The signal is profiling first, then applying the cheap, no-retrain levers in the right order. Here is the diagnosis-then-optimize playbook for p99.

A clean fundamentals question that catches people who only know GPT. The signal is matching the architecture to the task by its attention pattern, and explaining why the field consolidated on decoder-only even for non-generation tasks. Here is that answer.

Attention is permutation-invariant, so without position information a transformer cannot tell word order. The signal is knowing why, and why the field moved from absolute sinusoidal encodings to relative ones like RoPE and ALiBi that extrapolate to longer contexts.

Evaluation is the hardest, most underrated part of shipping LLMs. The signal is knowing why public benchmarks mislead, why LLM-as-judge is biased, and how to build a task-specific eval you actually trust.

Every customer asks how to stop the model making things up. The weak answer is 'better prompts.' The signal is knowing why models hallucinate and layering grounding, abstention, and measurement into a system you can defend.

Moderation is a multi-stage classification problem with brutal tradeoffs: false negatives cause real harm, false positives censor legitimate users, and the adversary adapts. The signal is the tiered pipeline, per-severity precision/recall calibration, and human-in-the-loop. Here is the design.

At thousand-GPU scale, hardware failure is the norm, not the exception, and a run that cannot resume wastes weeks. The signal is checkpointing strategy, deterministic resume, and minimizing lost work. Here is the fault-tolerant design.

Perplexity is the classic LM metric. The signal is knowing it is just exponentiated cross-entropy, what it intuitively measures, and why a low perplexity does not mean a good product.

Offline metrics gate releases on a fixed dataset, online metrics show what users actually did, and offline wins routinely fail to hold. Why you need both.

This is two questions hiding as one: how would you have caught it before full rollout, and how do you debug it now. Answer both and you signal senior judgment. Here is the staged-rollout and root-cause playbook.