Hugging Face AI & ML Engineer interview questions

Fine-tuning a 65B model used to mean a node of A100s. QLoRA collapses it onto one card with three specific tricks. The signal is knowing what gets quantized, what stays trainable, and why quality barely moves. Here is the answer.

Combine several fine-tunes into one model by doing arithmetic on their weights, with no retraining and no data. The signal is knowing why it works and which method to reach for when merges interfere.

When a drug name or a Hindi word becomes nine subword tokens, you pay in cost, context, and accuracy. Adding tokens is easy and adding them well is not. Here is the tradeoff.

A few thousand carefully chosen examples can beat a million scraped ones. The signal is knowing which filters matter, how you measure example quality, and why diversity beats raw volume.

DPO is simple and stable, but it can push down the probability of chosen responses, overfit the preference margin, and amplify length bias. The signal is naming these and the variants that fix each.

Distillation is not one method. The signal is knowing when to match logits versus train on generated text, why on-policy distillation beats off-policy, and how reasoning models are distilled.

The ratio of web, code, books, and math in pretraining quietly decides downstream skills. The signal is knowing how mixtures are chosen, why upsampling helps, and how methods like DoReMi automate it.

If a benchmark leaked into pretraining, the score measures memorization, not ability. The signal is the detection methods (n-gram overlap, canaries, perturbation tests) and why fresh held-out evals matter.

The same model can score ten points apart on MMLU depending on prompt format and scoring method. The signal is knowing the knobs (log-prob vs generation, few-shot, normalization) that make evals reproducible.

Both squeeze an LLM to 4-bit weights, but they decide what to protect very differently. The signal is GPTQ's error-correcting solve versus AWQ's activation-aware scaling, and the calibration each needs.

GGUF is the format behind most local LLM runs, and its quant names look cryptic. The signal is decoding what Q4_K_M means and why mixed-precision k-quants beat naive uniform quantization.

Grammar-constrained decoding sounds like a solved problem until tokenization misaligns with the grammar. The signal is the token-vs-character boundary mismatch and how prefix automata and token healing fix it.

Modern embedding models want a task instruction prepended to the text, and getting the prefix wrong quietly wrecks retrieval. The signal is why instructions help and the asymmetric query/passage convention.

A model that tops MTEB can still flop on your jargon-heavy corpus, and most teams jump straight to the expensive fix. There is a ladder, and the cheap rungs are the ones people skip. Here is the order to climb it.

Two ways to give an agent an action space: emit a JSON tool call per turn, or emit a program that calls the tools itself. One collapses N round-trips into one. The other is the one you can actually audit. Here is how to choose.

Comparing every pair of a million documents is a trillion comparisons. MinHash estimates Jaccard similarity from a tiny signature, and LSH turns dedup into a near-linear scan. Here is the implementation.

Every chat model's 'temperature' and 'top_p' knobs are a few lines of logit surgery. Implementing them shows you understand decoding, not just call it. Here is the from-scratch version.

Almost every modern training run uses warmup-then-cosine, and getting it wrong destabilizes early training or wastes the tail. It's a closed-form function of the step. Here is the implementation and the why.

A from-scratch test of subword tokenization. The signal is greedy longest-match encoding against a fixed vocabulary, the continuation-prefix convention, and how WordPiece differs from BPE. Here is the implementation.

Small labeled sets are the normal starting condition, not an excuse. The strong answer is a ladder of techniques that squeeze signal from unlabeled data, pretrained models, and the labeling budget. Here it is.

Naive 4-bit quantization can tank quality, and the reflex to give up and serve fp16 leaves a big speedup on the table. The accuracy is usually recoverable. Here is the ladder of fixes.

Three batching strategies, three very different latency profiles. Picking wrong leaves throughput or tail latency on the floor. Here is what each one costs and when to use it.

Most models do not fill a GPU, so one model per GPU burns money. The three sharing mechanisms (MIG, MPS, swapping) have very different isolation and overhead. Here is how to pick.

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.

A favorite practical screen at the labs, because it tests concurrency, time handling, and judgment in 20 lines. The trap is the background thread that wastes CPU. Here is the lazy-refill version interviewers want, plus the follow-ups.

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.

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.

These are the knobs every LLM product tunes, and the interviewer wants to know what each does to the probability distribution and which setting fits which task. Including why factual and creative tasks want opposite settings.

A classic that tests whether you reach past sorting for the right structure. The signal is the heap solution (O(n log k)), the bucket-sort O(n) trick, and how it changes for an unbounded stream. Here is the answer.

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.