NLP Engineer Interview Questions UK: 8 Real Questions and What Interviewers Want

Most NLP Engineer interviews in the UK follow a pattern, and knowing it beats guessing. Below are eight questions from our bank of 12, each with a note on what the interviewer is really after.

Eight NLP Engineer interview questions, and what the interviewer is thinking

1. How does tokenisation work in modern language models, and what problems can it cause?

Why they ask: Tokenisation underlies every NLP system, and mistakes here cause subtle failures.

2. When would you fine-tune an encoder model such as BERT for a task rather than prompting a large language model?

Why they ask: A real daily decision in NLP engineering, balancing cost, accuracy and control.

3. Walk me through how you would design the retrieval side of a RAG system and improve its quality.

Why they ask: RAG is now very common in NLP roles, and quality depends on retrieval details.

4. How do you evaluate NLP models, and what are the limits of metrics such as ROUGE, BLEU and accuracy?

Why they ask: Choosing meaningful evaluation is a key skill and a frequent weakness.

5. What is parameter-efficient fine-tuning, and how would you use LoRA in practice?

Why they ask: Tests knowledge of practical, cost-aware adaptation of large models.

6. Tell me about an NLP pipeline you built end to end, and the main challenges you faced.

Why they ask: They want evidence you can take NLP work beyond notebooks into something useful.

7. Your text classifier performs well in testing but struggles with abbreviations, slang and regional UK phrasing in real user messages. What do you do?

Why they ask: They want to see data-centred problem solving for domain and language variation.

8. What does a typical day as an NLP Engineer involve, and what draws you to the field?

Why they ask: They want a realistic view of the job and your genuine interest.

A sample answer to question 1

How does tokenisation work in modern language models, and what problems can it cause?

Modern models use subword tokenisation, such as byte-pair encoding, WordPiece or SentencePiece unigram models, which break text into frequent fragments learned from a corpus. This keeps the vocabulary manageable, typically 30,000 to 100,000 or more tokens, while avoiding out-of-vocabulary problems because rare words are split into smaller pieces and byte-level fallbacks can handle anything. The problems are practical. Text in languages or domains underrepresented in the tokeniser's training data gets split into many tokens, which raises cost and shortens effective context. Numbers, code, URLs and whitespace can be tokenised inconsistently, hurting arithmetic or formatting tasks. Misspellings and unusual casing change tokenisation unpredictably. And I must always use the exact tokeniser that matches the model, or results degrade silently. For a specialist domain such as clinical notes, I would check how technical terms are split, and consider domain-adapted models or extending the vocabulary where it makes a measurable difference.

Common mistake to avoid: Treating tokenisation as just splitting on spaces; mixing tokenisers and models

Want the rest?

Full model answers, key points and common mistakes for all 12 questions are on the NLP Engineer interview questions page. The first five are free, no sign-up needed.