NLP engineer interviews in 2026 go well beyond transformer theory. Expect questions on retrieval systems, generation quality metrics, tokenization trade-offs, RAG architecture, hallucination mitigation, and the production realities of deploying language systems at scale. This guide covers the full scope with answer frameworks and sample responses for the questions that determine hiring decisions.
Start Free Practice Interview →NLP engineering has changed more in the last three years than in the previous decade. The role used to center on feature engineering, classical pipelines, and task-specific models. Today, NLP engineers work across a spectrum — from fine-tuning foundation models and building retrieval-augmented generation systems to designing evaluation frameworks that catch hallucinations before they reach users.
This shift means interviews have changed too. You'll still be asked about attention mechanisms and embeddings, but you'll also face questions about retrieval ranking metrics, tokenizer artifacts, production monitoring for language systems, and how to evaluate generation quality when there's no single correct answer. The strongest candidates combine deep understanding of how language models work with practical judgment about when to use them — and when not to.
This guide is organized by interview topic area: language understanding and classification first, then retrieval and ranking, generation evaluation, tokenization depth, production systems, and coding implementation questions.
The NLP engineer role has broadened to encompass both traditional language processing and modern LLM-powered systems. While some companies still have dedicated NLP teams focused on task-specific models, many NLP engineers now work across the full stack of language technology.
Text understanding and classification systems — building models for sentiment analysis, intent classification, topic categorization, and content moderation. This includes both fine-tuned models for high-volume production use and LLM-based approaches for complex classification tasks.
Information extraction and structured output — named entity recognition (NER), relation extraction, event detection, and converting unstructured text into structured data. These tasks require understanding of sequence labeling architectures and evaluation at the entity/span level.
Retrieval and search systems — building the language understanding layer for search: query understanding, document ranking, semantic similarity, and increasingly, retrieval-augmented generation (RAG). This bridges traditional IR (BM25, TF-IDF) with neural approaches (dense retrieval, cross-encoders).
Generation and summarization — controlling LLM output quality for production use cases. This involves prompt engineering, fine-tuning, evaluation framework design, and hallucination mitigation.
Evaluation and quality assurance — designing metrics and evaluation pipelines that catch quality regressions, hallucinations, and safety issues before they reach users. This is increasingly the most critical NLP engineering skill.
NLP engineer overlaps significantly with LLM engineer and AI engineer. The clearest way to think about it: NLP engineers care about linguistic quality and language-specific evaluation, LLM engineers care about model infrastructure, and AI engineers care about application integration.
| Dimension | NLP Engineer | LLM Engineer | AI Engineer |
|---|---|---|---|
| Core focus | Language understanding, retrieval, generation quality, and NLP-specific evaluation | Model serving infrastructure, inference optimization, quantization, and scaling | Application layer — integrating models into products via APIs, RAG orchestration, user-facing features |
| Typical interview questions | Compare BM25 vs dense retrieval, evaluate summarization quality, design a NER system, explain tokenizer trade-offs | Optimize KV cache, design multi-tenant serving, compare quantization approaches, reduce inference latency | Design a RAG pipeline with fallbacks, handle rate limits across providers, evaluate end-to-end user experience |
| Model interaction | Fine-tunes for specific language tasks, designs evaluation and prompt strategies, builds retrieval pipelines | Serves and optimizes models at scale — inference engines, batching, distributed deployment | Consumes models via APIs, focuses on orchestration, context management, and product integration |
| Metrics focus | BLEU, ROUGE, F1 for NER, MRR/nDCG for retrieval, faithfulness for RAG, perplexity | Tokens per second, latency p50/p99, GPU utilization, cost per query | Task completion rate, user satisfaction, end-to-end latency, cost per interaction |
| Data focus | Text preprocessing, tokenization, data quality for training, annotation for NER/classification | Training data pipeline efficiency, data loading, distributed training data sharding | Context window management, document chunking strategy, retrieval relevance |
| Production concerns | Generation quality monitoring, hallucination detection, evaluation regression, safety filtering | Serving reliability, autoscaling, model versioning, A/B testing infrastructure | Fallback strategies, cost optimization across providers, user experience under failure |
These questions test your foundational understanding of how language models process and classify text. Even in the LLM era, understanding the mechanics of attention, embeddings, and task-specific architectures is expected.
This is a foundational NLP question that reveals whether you understand the evolution from fixed representations to context-dependent ones.
Static embeddings assign one vector per word regardless of context. Contextual embeddings produce different representations based on surrounding tokens. Static still useful for: extremely high-throughput low-latency applications, lightweight classifier features with limited training data, similarity search at massive scale, and baselines.
Static embeddings like Word2Vec and GloVe learn one fixed vector per token from co-occurrence statistics. The word 'bank' gets the same representation regardless of context. Contextual embeddings from BERT produce different representations depending on surrounding context — each token's embedding is a function of the entire input. I'd still use static embeddings in three scenarios. First, when inference latency makes transformers impractical — a pre-computed embedding lookup is orders of magnitude faster for millions of queries per second. Second, when building features for a lightweight downstream model with very few labeled examples — frozen Word2Vec plus logistic regression can outperform fine-tuned BERT with only hundreds of examples. Third, as a baseline — if static embeddings solve the problem, there's no reason to add transformer complexity and cost.
Understanding the architectural difference between encoders and decoders is fundamental to choosing the right model.
BERT (encoder): bidirectional attention, pretrained with masked language modeling. Strong for classification, NER, similarity. GPT (decoder): causal attention, pretrained for next-token prediction. Strong for generation, few-shot prompting. Choose encoder when you need rich bidirectional representations and have labeled data. Choose decoder when you need generation or in-context learning. Encoder-decoder models (T5, BART) combine both for sequence-to-sequence tasks.
This tests end-to-end system design for a common NLP use case.
Problem definition: how many categories, labeled data, latency requirements, how often categories change. Data: class distribution, annotation quality, multi-label needs. Model: pretrained encoder fine-tuned for many categories with sufficient data; LLM with prompt-based classification for dynamic categories. Evaluation: per-class F1, confusion matrix, confidence calibration. Production: monitor prediction distribution shift, build feedback loop from misrouted tickets.
NER is a core NLP task, and the evaluation distinction reveals whether you understand the task's structure.
NER identifies and classifies entities using BIO/BILOU tagging. Token-level accuracy treats each token independently. Span-level F1 requires both boundary and type to be correct — a partial entity extraction counts as incorrect. Span-level is the standard because partial entities are often useless downstream. CRF layers help with consistency by modeling tag transition constraints. Report per entity type since performance varies across categories.
This tests engineering maturity — whether you always reach for the most complex approach.
TF-IDF + logistic regression wins when: very small training data (hundreds of examples), extremely tight latency, interpretability matters, simple task where bag-of-words captures the signal, or you need a fast baseline. Examples: email spam, language detection, simple sentiment on short reviews. Key insight: always train the simple baseline first.
Data leakage in NLP is subtler than in tabular ML.
Common patterns: (1) document-level contamination — split by document not sample; (2) temporal leakage — use temporal splits; (3) near-duplicate text — deduplicate with MinHash before splitting; (4) label leakage via keywords — check if bag-of-words baseline achieves suspiciously high accuracy; (5) test set contamination in pretrained models — use post-release evaluation data, run memorization checks.
Retrieval is one of the most tested areas in modern NLP interviews. With RAG becoming a standard architecture pattern, interviewers expect you to understand both classical information retrieval and neural approaches, and to reason about how they combine.
BM25 is the baseline for information retrieval. Understanding why it persists shows practical judgment.
BM25 scores documents based on term frequency, inverse document frequency, and document length normalization. Still widely used because: zero training data needed, very fast (inverted index lookup), strong lexical matching for exact terms, and interpretable. Weaknesses: no semantic understanding, poor on short queries with vocabulary mismatch. This is why hybrid systems (BM25 + dense retrieval) are the standard.
BM25 is a term-matching ranking function based on three intuitions: documents containing query terms more frequently are more relevant (TF), terms appearing in fewer documents are more informative (IDF), and longer documents shouldn't be unfairly penalized (length normalization). It's still the default first-stage retriever for three reasons. First, zero training data needed — you can deploy on a new corpus immediately, while dense retrieval needs a trained bi-encoder. Second, speed: BM25 uses an inverted index, so retrieval time scales with unique query terms, not corpus size. Third, lexical reliability: searching for 'ERR-4029' or a product SKU, BM25 finds it perfectly, while dense retrieval might not preserve exact string matches. The weakness is semantic — 'how to fix a flat tire' won't match 'puncture repair.' In production, I almost always use a hybrid approach: BM25 for lexical recall plus a dense retriever for semantic recall, with a re-ranker on top.
Dense retrieval is the neural counterpart to BM25. Understanding both approaches is essential.
Bi-encoder: encode query and document independently into dense vectors, rank by cosine similarity. Document embeddings pre-computed and indexed (FAISS). Fast but query-document interaction is limited. Cross-encoder: concatenate query and document, output relevance score. Much more accurate but O(N) inference — too slow for first-stage retrieval. Standard pattern: bi-encoder retrieves top-k candidates, cross-encoder re-ranks them.
RAG is the dominant architecture for grounding LLMs. This tests full pipeline reasoning.
Key decisions: (1) Chunking strategy — too small lacks context, too large dilutes signal; (2) Retrieval — hybrid BM25 + dense with re-ranker; (3) Context assembly — how many chunks, ordering, metadata; (4) Generation — prompt design for grounded responses, handling insufficient context; (5) Evaluation — retrieval recall@k, generation faithfulness, answer quality. Failure modes: retriever misses relevant docs, model ignores context for parametric memory, model fabricates details.
A RAG system has three stages with critical design decisions. First, indexing: chunk documents with semantic paragraph splitting (256-512 tokens with overlap), embed each chunk, store in a vector database. Second, retrieval: hybrid approach — BM25 for lexical matching plus bi-encoder for semantic, reciprocal rank fusion to combine, then cross-encoder re-ranks top 20-30 candidates down to 5-8. The re-ranker is critical — it catches cases where the bi-encoder missed relevance or BM25 over-weighted a spurious keyword. Third, generation: the prompt explicitly instructs the model to answer from provided context and say 'I don't know' when unsure. Include source metadata for citations. For evaluation, I track three metrics separately: retrieval recall@k, faithfulness (is the answer supported by retrieved content?), and end-to-end answer quality. The most common failure mode is the model hallucinating details that sound plausible but aren't in the retrieved context — faithfulness evaluation is the primary defense.
Retrieval metrics are different from classification metrics. This tests whether you can evaluate search properly.
MRR: find the rank of the first relevant result, compute 1/rank, average across queries. Best for tasks where only the first correct result matters — QA, navigational search. nDCG: accounts for multiple relevant results with graded relevance, discounts by log position, normalizes against ideal ranking. Best for tasks where multiple results matter — document search, product ranking. Also mention Recall@k — critical for RAG evaluation.
RAG failures are subtle because multiple components interact. This tests systematic debugging.
Three primary failures: (1) Retriever miss — relevant document wasn't retrieved. Check recall@k. Fix retrieval. (2) Retrieved but ignored — correct chunk is in context but model used parametric memory. Fix prompt, reorder context, use better instruction-following model. (3) Reranker degradation — pushes relevant document down. Compare recall before/after reranking. Diagnostic principle: evaluate each stage independently. If recall@20 is high but answer quality is low, the problem is downstream.
Tests practical evaluation methodology and systematic improvement.
Step 1: build evaluation set with human-judged relevance. Step 2: measure baseline (MRR, nDCG, Recall@k) by query type. Step 3: error analysis — categorize failures (vocabulary mismatch, intent misunderstanding, stale index, ranking errors). Step 4: targeted improvements based on failure type. Step 5: A/B test improvements with online metrics.
Generation evaluation is one of the hardest problems in NLP — there's rarely a single correct answer, and automated metrics capture only part of quality. These questions test whether you understand the metrics landscape and can design evaluation systems that work in production.
Standard generation metrics that many candidates can't explain precisely.
BLEU: n-gram precision with brevity penalty. Standard for MT. ROUGE: n-gram recall. ROUGE-L uses longest common subsequence. Standard for summarization. METEOR: includes synonym/stemming matching with fragmentation penalty. Better human correlation. Key limitation: all measure surface overlap, not meaning. A factually wrong but well-worded output scores well. Pair with human evaluation or LLM-as-judge.
BLEU measures n-gram precision: what fraction of n-grams in the generated output also appear in the reference. ROUGE measures n-gram recall: what fraction of reference n-grams appear in the output. ROUGE-L uses longest common subsequence for better word-ordering capture. METEOR extends beyond exact matching to include synonyms, stemming, and paraphrases, with a fragmentation penalty favoring contiguous matches. The critical limitation they all share is measuring surface-level text overlap, not meaning. A factually wrong summary using the right vocabulary scores well. A correct summary in different words scores poorly. This is why I never use these alone in production. I pair them with human evaluation or LLM-as-judge. For faithfulness specifically — whether output is supported by source material — I use NLI-based metrics rather than n-gram overlap.
The standard intrinsic metric for language models.
Perplexity is exponentiated average negative log-likelihood per token. Lower is better — measures how 'surprised' the model is. What it tells you: how well the model predicts text from a specific distribution. What it doesn't: task performance, generation quality, factual accuracy, and it's not comparable across different tokenizers (different vocab sizes produce different scales).
Hallucination is the central quality challenge. This tests whether you have a real strategy.
Evaluation: NLI-based metrics, extractive overlap, LLM-as-judge (needs calibration), human evaluation. Mitigation: (1) fix retrieval quality first — garbage in, garbage out; (2) explicit prompt instructions for grounded responses; (3) lower temperature; (4) require citations; (5) post-generation verification against source; (6) confidence-based routing to human review.
Conversational systems combine multiple NLP capabilities and require multi-dimensional evaluation.
Multi-dimensional: task completion, factual accuracy, coherence across turns, safety, latency. Methods: automated metrics, LLM-as-judge for full conversations, human evaluation (Likert ratings, A/B preferences), production metrics (engagement, completion rate, escalation rate). Key insight: single-turn metrics don't capture conversation quality — evaluate full conversations, not individual turns.
Tokenization is the foundation of every NLP pipeline. Modern interviews go deeper than 'what is tokenization' — they test whether you understand the trade-offs between tokenizer algorithms, the problems tokenizers introduce, and how tokenization decisions affect downstream performance.
Tokenization choice affects model performance, multilingual capability, and deployment. Understanding the algorithms shows NLP depth.
BPE: starts with characters, iteratively merges the most frequent pair. Greedy, deterministic. GPT-family uses byte-level BPE. WordPiece: merges based on likelihood maximization, not raw frequency. Used by BERT. Unigram: starts large, iteratively removes least-impact tokens. Can produce multiple valid tokenizations (enables subword regularization). Used by SentencePiece/T5. Practical: vocabulary size affects model size and context window utilization. All three produce unintuitive splits that affect model behavior.
All three are subword tokenization algorithms — they split words into frequent subword units rather than whole words or characters. The difference is how they build the vocabulary. BPE starts with the character set and iteratively merges the most frequent adjacent pair. It's greedy — always picks the globally most frequent merge. GPT-family models use byte-level BPE, operating on bytes rather than unicode characters, ensuring any text can be tokenized without unknown tokens. WordPiece merges the pair that maximizes training data likelihood rather than raw frequency. The practical difference from BPE is small for English but matters more for multilingual models. BERT uses this. Unigram works in the opposite direction — starts with a very large vocabulary and iteratively removes tokens that least impact training data likelihood. The key practical difference is that Unigram can produce multiple valid tokenizations of the same input, enabling subword regularization during training. T5 uses Unigram via SentencePiece. For practical impact: vocabulary size is the main lever. Larger vocabulary means fewer tokens per text (faster inference, more context fits in the window) but a larger embedding table. And all tokenizers produce artifacts — 'New York' might tokenize differently depending on spacing and context.
Tokenizer artifacts are a real source of model failures that many engineers overlook.
Problems: (1) inconsistent tokenization — same word may tokenize differently based on context, creating inconsistent representations; (2) multilingual unfairness — tokenizers trained on English fragment non-English text into many more tokens, consuming more context window and increasing cost; (3) numerical handling — numbers split inconsistently, making arithmetic unreliable; (4) code and technical content — variables, URLs, code fragment into meaningless subwords; (5) rare/new terms — domain jargon fragments into many small tokens. Mitigations: vocabulary augmentation for domain terms, separate number handling, and awareness that context window utilization varies across languages.
Preprocessing directly affects model quality. This tests systematic approach vs ad-hoc cleaning.
Pipeline by importance: (1) Deduplication — near-duplicates inflate metrics and waste compute. Use MinHash/SimHash. (2) Language/quality filtering — remove non-target language, boilerplate, low-quality text. (3) Normalization — unicode, whitespace, encoding fixes. (4) PII handling — mask personally identifiable information. (5) Domain-specific cleaning — HTML removal, URL handling, emoji normalization. (6) Tokenization — applied by the model. Key pitfall: over-preprocessing. Lowercasing destroys casing info for NER. Removing punctuation destroys sentence boundaries. Aggressive filtering removes minority dialects. Always evaluate preprocessing impact on downstream performance.
Production NLP questions test whether you've shipped language systems and dealt with challenges that don't appear in research papers — monitoring, regressions, safety, cost, and the reality that models degrade over time.
NLP systems degrade in subtle ways that uptime monitoring doesn't catch.
Monitoring layers: (1) Input — distribution of lengths, language mix, topic distribution. Shifts indicate changing users or use cases. (2) Output — confidence distribution, output lengths, category distribution. Confidence shift suggests unfamiliar inputs. (3) Quality — sample outputs for human evaluation or LLM-as-judge at regular cadence. (4) Latency — p50/p95/p99. (5) Business metrics — task completion, satisfaction, escalation rates. Problem signals: confidence distribution shift, output distribution shift, quality score degradation, latency increases.
This is a daily production decision. Using the biggest model for every request is expensive; the smallest loses quality.
Strategies: (1) Tiered routing — small fast model for simple queries, large model for complex. (2) Caching — semantic caching by embedding similarity, not just exact match. (3) Model selection — smaller fine-tuned models often match larger general models on specific tasks. (4) Batching — batch requests for GPU utilization. (5) Quantization — INT8/INT4 with minimal quality loss. (6) Prompt optimization — shorter prompts reduce cost and latency. (7) Streaming — stream tokens to reduce perceived latency.
Safety and privacy are non-negotiable in production.
Safety: input filtering (detect/block harmful inputs), output filtering (scan for harmful content before returning), prompt injection defense (enforce constraints in application layer, not model), red teaming (test adversarial inputs). PII: input-side detection and masking before logging, output-side scanning for PII leakage, logging controls ensuring PII isn't stored without anonymization. Use dedicated libraries like Presidio rather than hand-written regex alone.
Tests whether you can assemble retrieval, ranking, generation, and infrastructure into a coherent production system.
Architecture: (1) Indexing — Elasticsearch for BM25 + vector store for dense retrieval. Semantic paragraph splitting, 256-512 tokens with overlap. (2) Retrieval — hybrid BM25 + bi-encoder, reciprocal rank fusion. (3) Reranking — cross-encoder on top 20-30 candidates, most impactful quality lever. (4) Generation — top 5-8 chunks to LLM with source metadata, citation instructions. (5) Caching — semantic cache for frequent queries. (6) Privacy — on-premise/private cloud, self-hosted model or DPA provider, PII detection on inputs and outputs. (7) Observability — log scores, latency, user feedback, periodic eval sets. Latency budget: retrieval 50-100ms, reranking 100-200ms, generation 500-2000ms, total p95 under 3 seconds.
Model updates are a real production challenge for LLM-powered systems.
The problem: prompts tuned to a specific model's behavior break when the model updates. Mitigation: (1) evaluation suites — comprehensive test cases run against every model update before deploying; (2) version pinning — pin to specific model versions, upgrade deliberately; (3) prompt regression testing — version control prompts, run tests on changes; (4) output format validation — check structural conformity regardless of model version; (5) gradual rollout — deploy to small traffic percentage first, compare metrics before full rollout.
NLP coding interviews test whether you can implement the building blocks of language processing systems. Expect questions involving text processing, evaluation metric computation, and working with tokenizers and embeddings programmatically.
ROUGE is the standard summarization metric. Implementing it tests whether you understand what it actually computes.
Tokenize both texts into words. ROUGE-1: compute unigram overlap. Precision = overlapping unigrams / generated unigrams. Recall = overlapping unigrams / reference unigrams. F1 = 2×P×R / (P+R). ROUGE-2: same with bigrams. Handle edge cases: empty texts, single-word texts, case-insensitive matching. Production implementations use stemming and handle stopwords.
BM25 is foundational to information retrieval. Implementing it tests whether you understand the ranking you've been discussing.
Precompute: document frequencies per term, average document length. Per query term in a document: TF component with saturation (tf × (k1+1)) / (tf + k1 × (1-b + b×dl/avgdl)), multiply by IDF (log((N-df+0.5)/(df+0.5)+1)). Sum across query terms. Standard: k1=1.2, b=0.75. Handle missing terms (score 0).
PII handling is a production requirement. Tests practical regex skills and understanding of rule-based vs ML-based detection.
Regex for structured PII: email patterns, phone number formats, SSN, credit card patterns. Replace with type-specific tokens ([EMAIL], [PHONE]). For names: regex is insufficient — names are ambiguous, culture-dependent, context-sensitive. Extend with NER-based detection (spaCy, fine-tuned BERT, or Presidio). Caveat: even NER isn't reliable enough for compliance. Production PII systems need NER + policy rules + human review.
NLP roles require collaboration with product teams, data labeling teams, and researchers. Behavioral questions test whether you can navigate ambiguity, communicate trade-offs, and make practical decisions when the 'correct' answer isn't clear.
The testing-to-production gap is a defining NLP challenge — evaluation sets don't capture all the ways users interact with language systems.
STAR format. Describe the model's purpose, what metrics looked good in testing, and what failed in production. Common patterns: distribution shift, adversarial inputs, scale effects (rare errors become common at high volume). Emphasize what you changed about your evaluation process, not just the model fix.
A practical decision NLP engineers make regularly. Reveals how you think about cost, complexity, and maintenance.
Start with prompting — faster iteration, no training data needed. Switch to fine-tuning when: consistent output format needed, latency/cost demands smaller model, sufficient labeled data available (hundreds to thousands), or task requires domain-specific knowledge. Fine-tuning: higher upfront cost, lower per-inference cost, better consistency. Prompting: lower upfront cost, higher per-inference cost, more fragile. Not binary — start with prompting to validate, fine-tune once data and confidence exist.
NLP systems often have subjective quality that stakeholders judge differently than engineers.
Show data — specific failure cases, error rates on representative examples, what the user experience looks like when the system fails. Quantify risk: not 'it might fail' but 'it fails on X% of queries, and failure looks like Y.' Propose alternatives: launch with guardrails (human-in-the-loop, narrower scope, disclaimers), limited beta. Show you're helping find a path to launch safely, not blocking the launch.
Reading frameworks helps, but NLP interviews reward the ability to reason through design trade-offs and explain evaluation strategies under pressure. Our AI simulator generates role-specific questions, times your responses, and scores both technical depth and communication clarity.
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Yes, but the emphasis has shifted. You should understand TF-IDF and BM25 (they're still the backbone of many retrieval systems), know when regex-based approaches outperform ML (structured extraction, input validation), and recognize that rule-based systems are often the right choice for well-defined tasks. Many production systems use a hybrid approach: rules for simple high-confidence cases and models for everything else. Showing you can choose the simplest effective approach demonstrates engineering maturity.
You should be able to explain self-attention mechanically (Q, K, V matrices, attention weights, multi-head attention) and understand why it works. You don't typically need to derive gradients from scratch — that's more deep learning engineer territory. Understand positional encoding, layer normalization, and feed-forward layers. The practical questions matter more: why attention scales quadratically with sequence length, and how efficient attention variants (FlashAttention, multi-query attention) address the bottleneck.
Increasingly yes. Most roles expect familiarity with LLMs via APIs, prompt engineering basics, and RAG architecture. Many also expect fine-tuning experience. Companies with large-scale NLP products still value traditional NLP skills heavily alongside LLM knowledge. Companies building LLM-powered applications may weight LLM experience more. The safest preparation covers both: strong fundamentals in classification, retrieval, and evaluation, plus practical LLM experience in prompting, fine-tuning, and RAG.
The core distinction is focus. An NLP engineer cares about linguistic quality — does the system understand language correctly, are entities extracted accurately, does retrieval return relevant results? An LLM engineer cares about model infrastructure — is inference fast enough, is serving scalable, is the quantized model still accurate? In practice the roles overlap significantly. If the role involves evaluation frameworks, retrieval systems, or text quality, it's NLP-leaning. If it involves serving infrastructure or inference optimization, it's LLM-leaning.
For NLP fundamentals: Hugging Face Transformers, spaCy, and PyTorch. For retrieval: familiarity with a vector database (FAISS, Pinecone, Weaviate) and a search framework (Elasticsearch). For LLM applications: LangChain or LlamaIndex. For evaluation: know how to compute metrics with libraries like evaluate or sacrebleu. Interviewers care more about understanding the concepts than knowing a specific framework's API.
Large tech companies emphasize scale — retrieval over billions of documents, classification at millions of requests per second. AI/LLM companies focus on generation, evaluation of open-ended outputs, and working with latest models. Startups test breadth — you may build the entire NLP pipeline, so interviews test practical judgment. Domain-specific companies (healthcare, legal, finance) add domain knowledge requirements and emphasize safety, compliance, and handling sensitive data.
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