The Best Questions To Ask in NLP Engineer Interviews

When you're hiring a natural language processing (NLP) engineer, your goal is to bring on the right person with the knowledge and experience that can best help your team. A key step in this process is asking quality questions during interviews.

This guide will show you the right NLP interview questions to ask in interviews as you evaluate different candidates. By the time you're done reading, you'll know exactly what you need to do to feel fully confident about the NLP engineer you're adding to your team.

NLP engineer interview basics

At some point during the interview (likely at the beginning), you'll want to ask questions to get a feel for the candidate's general knowledge of computer science and natural language understanding (NLU) related topics. We cover some of the straightforward NLP interview questions to consider.

What experience do you have working with Python?

Python is a programming language often used in data analysis and building software. Python is essential for completing a host of NLP tasks, and many common Python tools and libraries are found inside the natural language toolkit (NLTK). This open-source forum is full of libraries, programs, and helpful resources to produce NLP programs.

Here's what you should look for:

• Whether the candidate has hands-on experience with Python, specifically in NLP or data workflows
• Familiarity with common NLP libraries (e.g., NLTK, spaCy, Gensim, Hugging Face Transformers)
• Ability to write clean, efficient, and modular Python code
• Experience with data preprocessing, scripting, and automation in Python
• Understanding of Python's ecosystem for machine learning (NumPy, pandas, scikit-learn, PyTorch, TensorFlow)
• Examples of past projects where Python was used to build, train, or deploy models

What do you know about machine learning?

In some ways, natural language processing is a subset of a larger discipline called machine learning, and both are critical to the development of artificial intelligence (AI) software. An NLP engineer should have a solid understanding of machine learning and its relationship with NLU and data science.

Here's what you should look for:

• A clear understanding of core machine learning concepts (e.g., supervised vs. unsupervised learning, model training, evaluation, feature engineering)
• Ability to explain how machine learning underpins NLP tasks such as classification, sequence modeling, and language understanding
• Familiarity with algorithms commonly used in NLP (e.g., logistic regression, decision trees, naive Bayes, SVMs, recurrent networks, transformers)
• Understanding of how ML, NLU, and data science intersect in real-world NLP systems
• Practical experience building or training ML models, not just theoretical knowledge
• Awareness of model limitations, training challenges, and overfitting
• Ability to articulate end-to-end ML workflows: data preparation, modeling, validation, and deployment

What type of statistical analysis work have you done so far?

Statistical analysis involves collecting and studying large volumes of data to spot trends and provide helpful insights and conclusions. NLP models often use statistical data to improve their operational capacity.

Here's what you should look for:

• Comfort with core statistical concepts (e.g., distributions, variance, probability, correlation, significance testing)
• Experience applying statistics to large datasets rather than only theoretical knowledge
• Ability to explain how statistical insights inform NLP tasks such as language modeling, feature extraction, or probability-based methods
• Familiarity with statistical techniques used in NLP (e.g., TF-IDF, n-grams, likelihood estimation, sampling methods)
• Demonstrated ability to use statistical analysis tools (pandas, NumPy, SciPy, or statistical packages in Python)
• Understanding of how statistical patterns influence machine learning techniques and model evaluation
• Examples of past work where statistics improved model accuracy, efficiency, or decision-making

Text preprocessing

NLP enables computers to process, analyze, and simulate the structures and meanings of words and phrases — and a key step in this process is text preprocessing. Text preprocessing transforms electronic text through tokenization, normalization, and cleaning to create an input a program can read, making proficiency in text preprocessing essential for NLP engineers. 

To assess a candidate's qualifications in this area, consider asking questions like those in the following sections.

What words would you consider essential "stop words"?

Stop words are frequently used articles and prepositions that search engines are trained to remove. If these words were left in search queries, they would require additional processing time and take up unnecessary database space. The specific words that a system considers "stop words" can vary from system to system, and it's interesting to hear each engineer's thoughts on what words should make this list for the best possible syntactic interpretation.

Here's what you should look for:

• Understanding that stop words vary by task, domain, language, and model goals (there is not a one-size-fits-all list)
• Awareness of how removing or keeping stop words affects downstream tasks such as search, classification, sentiment analysis, or translation
• Ability to articulate when not to remove stop words (e.g., sentiment tasks where words like "not" shift meaning)
• Familiarity with customizing stop word lists rather than relying solely on defaults from libraries like NLTK or spaCy
• Practical examples of how they've handled stop word preprocessing in past NLP projects
• Critical thinking about trade-offs: speed vs. accuracy, storage vs. semantic value
• Understanding of how stop-word removal fits into the broader preprocessing pipeline

Do you prefer to use stemming or lemmatization?

Stemming removes prefixes and suffixes from a word to find the basic root word (or "stem"). Through stemming, a computer will process English words like "organizing," "organized," and "organizer," all based on the same base form of "organiz." Using base forms aids in normalization, makes text easier to process, and allows the end result to happen more quickly than lemmatization.

Lemmatization is similar in that it attempts to find a root word and helps with normalization. Its practical difference from stemming is that it finds a word's part of speech in the sentence, and looks for more than prefixes and suffixes. So, for example, lemmatization will group the words "goose" and "geese," as well as "sing," "sang," and "sung." Lemmatization tends to produce a more coherent word or sentence that is more natural and easier to understand. 

Usually, stemming is preferred when speed is the end goal, while lemmatization is better when you're hoping to create an end result with a grammatical structure that is understandable for the average person.

Another relevant topic is word embedding, or converting words into numeric representations. Word embedding allows words with similar meanings to have the same numerical assignment or representation. When you incorporate word embedding, you can process data more quickly. You may also talk about part-of-speech (POS) tagging here, but POS tagging could also be relevant within NLP tasks and applications.

These are complex topics, and to help ensure your candidate is knowledgeable in them, you should look for:

• A clear understanding of the difference between stemming (fast, rule-based, less precise) and lemmatization (slower, linguistically informed, more accurate)
• Ability to choose the appropriate method based on project goals
• Awareness of how normalization impacts downstream NLP tasks such as classification, topic modeling, and search
• Knowledge of libraries and tools that perform both tasks (e.g., NLTK, spaCy, Stanza)
• Understanding that lemmatization often relies on POS tagging and why this improves accuracy
• Insight into how normalization interacts with modern embedding models (e.g., recognizing that many transformer models reduce the need for aggressive stemming)
• Practical examples where the candidate selected one approach over the other, and why

How does tokenization impact the rest of your NLP pipeline?

Tokenization is usually viewed as the first component of an effective NLP pipeline. Through word tokenization, large chunks of text are broken down into individual words, sentences, and other meaningful elements for better semantic analysis. In some cases, tokens may be referred to as n-grams, with the "n" representing the number of words, texts, or symbols in succession.

Asking a question about n-grams or tokenization will help you see how a candidate usually structures their tokenization process and uses it to feed into the rest of their syntactic (i.e., grammatical) and semantic (i.e., meaningful) analysis. You'll also gain clarity about how they could use tokenization to support the best possible syntax or machine translation.

Here's what you should look for:

• Understanding that tokenization is foundational and influences every downstream step (e.g., embeddings, POS tagging, model accuracy)
• Ability to explain different tokenization approaches like word-level, sentence-level, subword tokenization (BPE, WordPiece), and character-level
• Awareness of how tokenization choices differ depending on the task (e.g., translation, sentiment analysis, search, chatbots)
• Insight into handling edge cases such as contractions, punctuation, emojis, multilingual text, or domain-specific symbols
• Familiarity with tokenization tools and libraries (spaCy, NLTK, Hugging Face tokenizers)
• Understanding of n-grams and how they affect context, sparsity, and model performance
• Ability to explain how tokenization supports semantic and syntactic analysis, especially in pipelines that depend on consistent input structure
• Practical experience designing or adjusting tokenizers for specific datasets or models

Advanced algorithms and models

Having a dialogue with questions and answers about advanced language models will help you evaluate a candidate's knowledge and experience in key tasks relevant to NLP engineers.

What neural networks have you built in the past?

Artificial neural networks are a subfield of machine learning. They enable computers to process datasets and allow them to gradually improve their functionality over time through training. These systems can recognize patterns and solve common problems. As the baseline of artificial intelligence and a key branch of machine learning, this skill set is essential for working in NLP.

When you ask a candidate about what neural networks they have built or have experience working on, you'll learn about their skills and experience in this area while also gaining insight into their beliefs and perspectives on the topic.

Here's what you should look for:

• Hands-on experience building or training neural networks, not just theoretical exposure
• Familiarity with architectures relevant to NLP (e.g., RNNs, LSTMs, GRUs, CNNs for text, and transformers)
• Ability to explain why they chose certain architectures for specific tasks
• Understanding of training processes: loss functions, optimizers, regularization, hyperparameter tuning
• Practical knowledge of deep learning frameworks such as PyTorch or TensorFlow
• Experience handling real-world challenges like overfitting, imbalanced data, long training times, or unstable gradients
• Ability to articulate lessons learned from past projects and how they would approach similar models today
• Evidence of end-to-end ownership: from dataset preparation, model development and evaluation, to deployment

Do you prefer supervised or unsupervised deep learning?

Deep learning automatically processes and extracts valuable findings from datasets, but how it works depends on the level of supervision or oversight the algorithms need. Data from unsupervised systems is raw and unlabeled. These systems require less ongoing supervision or intervention, and can glean powerful insights from large quantities of data, but the algorithms may be less predictable or accurate after training.

On the other hand, supervised deep learning models require more attention but can produce more refined systems. They are trained on a process- or rule-based system that annotates or labels the unstructured data, and have a clear optimization objective. This enables them to be trained until their output meets specified criteria.

This may also be the time to discuss information extraction, or the automated selection of specific data points from a body of text. Since information extraction involves developing and using specific methods, it's a relevant topic to address alongside deep learning. Remember that this is different from information retrieval, or the process of returning information to the user.

Here's what you should look for:

• Clear understanding of the differences between supervised and unsupervised deep learning, including strengths and limitations of each
• Ability to match the right learning approach to the right NLP task (e.g., supervised for classification tasks, unsupervised for clustering or topic modeling)
• Awareness of hybrid or semi-supervised methods and when they may be beneficial
• Practical examples showing experience training both types of models
• Understanding of data requirements, labeling challenges, and resource implications
• Insight into how supervision levels affect model accuracy, interpretability, reliability, and scalability
• Evidence that they can reason about trade-offs, such as speed vs. accuracy or automation vs. control

How familiar are you with transformer models?

Transformer models are specific neural networks that learn how to track relationships in various types of data. They use advanced mathematical techniques to spot various data elements and how they influence each other.

Here's what you should look for:

• Understanding of the core transformer architecture (self-attention, positional encoding, and encoder/decoder structure)
• Familiarity with major transformer-based models (BERT, GPT, RoBERTa, T5, XLNet, DistilBERT, etc.)
• Ability to explain why transformers outperform older architectures like RNNs or LSTMs on many NLP tasks
• Experience fine-tuning or training transformer models using frameworks like Hugging Face Transformers
• Awareness of computational requirements, memory constraints, and optimization strategies for large models
• Understanding of how transformers are applied to tasks such as sentiment analysis, translation, summarization, NER, or question answering
• Ability to discuss real-world challenges like limited data, long training times, token limits, or domain adaptation
• Insight into responsible use: bias, hallucination risks, interpretability, and ethical considerations

NLP tasks and applications

These NLP interview questions will allow your candidate to share how they could achieve specific goals or outcomes by putting their knowledge and experience within NLP into practice.

How do you incorporate sentiment analysis?

Sentiment analysis goes beyond the text on the page and seeks to interpret the tone behind the message. It enables NLP systems to evaluate whether a message is positive, negative, or neutral, which impacts the response it generates. Sentiment analysis also helps capture information objectively, although further training may be necessary to ensure that systems avoid any potential biases.

To take your conversation a step further, you can make connections to related disciplines such as semantics analysis, dependency parsing, or part-of-speech tagging. These are all important components of text summarization and speech recognition.

Here's what you should look for:

• An understanding of different sentiment analysis approaches (rule-based, traditional ML, and transformer-based models)
• Ability to explain how sentiment labels are generated and validated
• Familiarity with challenges such as sarcasm, domain-specific language, slang, and context dependence
• Awareness of bias in sentiment datasets and how to mitigate it through balanced data, fine-tuning, or post-processing
• Experience integrating sentiment analysis into larger NLP pipelines such as chatbots, summarization tools, or customer feedback systems
• Ability to connect sentiment analysis to related concepts like semantic analysis, dependency parsing, and POS tagging
• Examples of past projects where they implemented or improved sentiment classification
• Ability to reason about metrics for evaluating sentiment models (e.g., accuracy, F1-score, confusion matrices)

What is your preferred method for NER training?

Named entity recognition (NER) is a component of NLP that involves identifying key information within the text and fitting it into a set of predetermined categories. To incorporate NER, the engineer could train the language model for multi-class classification or implement a conditional random field through an NLP speech tagger and the natural language toolkit. Each option has its benefits and downsides, and you can learn a lot about a candidate by hearing what approach they might take.

Here's what you should look for:

• Understanding of different NER approaches: rule-based systems, classical ML models, CRFs, BiLSTM-CRF architectures, and transformer-based models (e.g., BERT for NER)
• Ability to explain why they would choose one method over another based on data size, domain complexity, and accuracy requirements
• Familiarity with annotation challenges and how to handle ambiguous entities, overlapping spans, or domain-specific terminology
• Experience with relevant tools and frameworks (spaCy, Hugging Face Transformers, Flair, and NLTK)
• Awareness of evaluation metrics for NER (precision, recall, F1 score, token-level vs. entity-level evaluation)
• Ability to discuss strategies for handling imbalanced data or rare entity types
• Examples of NER models they've built, improved, or fine-tuned, including lessons learned
• Understanding of downstream dependencies, including how NER outputs feed into search systems, chatbots, information extraction, or knowledge graphs

How many NLP chatbots have you created in the past?

NLP chatbots have the potential to process, analyze, and respond to human prompts in a way that mimics natural conversation with human language. To take this question further, you could ask the candidate to describe one specific chatbot they are especially proud of or one they wish they had developed differently based on the experience they have now.

Here's what you should look for:

• Hands-on experience building chatbots rather than only conceptual knowledge
• Ability to describe the chatbot's purpose, architecture, and NLP components (intent classification, entity extraction, dialogue management)
• Familiarity with frameworks like Rasa, Dialogflow, Botpress, Microsoft Bot Framework, or custom transformer-based pipelines
• Understanding of conversational design principles such as fallback handling, context tracking, and user intent disambiguation
• Ability to explain challenges they faced, including ambiguous queries, multilingual support, and domain adaptation, and how they solved them
• Evidence of iterative improvement: versioning, retraining, error analysis, A/B testing
• Insight into evaluation metrics for chatbots (intent accuracy, task completion rate, user satisfaction)
• Willingness to reflect on what they would improve with current knowledge, showing maturity and growth

Evaluation metrics and performance

A great NLP engineer will ensure that your existing systems run smoothly while also looking for ways to improve them. These questions and answers will help you understand how effectively a candidate could assess the effectiveness of NLP models and enable them to run even better than before.

How do you improve existing TF-IDF models?

Term frequency-inverse document frequency (TF-IDF) measures how relevant a particular word is inside a sentence, paragraph, or document. The rating will increase the more often a word occurs, and the general commonality of the word is also considered.

By improving TD-IDF models, you'll create NLP systems that are more efficient and effective. When you ask a candidate this question, you'll have a chance to gauge what they already know about TF-IDF while also hearing their thoughts about how the tool can be used in the best way possible.

You may also ask about candidates' understanding of the bag of words model, since the bag of words also has to do with the frequency of word occurrence. Latent semantic indexing may also be relevant here.

Here's what you should look for:

• Understanding of how TF-IDF works and its limitations, especially with respect to context, synonymy, and sparsity
• Experience improving TF-IDF representations through techniques such as adjusting tokenization rules, customizing stop word lists, and applying n-grams
• Ability to explain when TF-IDF is appropriate and when more advanced embeddings (Word2Vec, GloVe, BERT) may be better
• Familiarity with related models like bag-of-words, LSA/LSI, and how they complement or overcome TF-IDF shortcomings
• Practical examples where they optimized TF-IDF for search, classification, recommendation, or clustering
• Awareness of evaluation methods to measure the impact of improvements
• Insight into computational trade-offs, such as dimensionality reduction using PCA, SVD, or truncated SVD

What data points are most important to review?

There are a number of data points you can consider when measuring the current effectiveness of NLP techniques and processing tools. Typically, these fall into two categories: intrinsic and extrinsic evaluators. Intrinsic evaluators consider the success of specific subtasks, and extrinsic evaluators look at the system as a whole.

Regardless, when you ask this question, you'll learn what the candidate values in system evaluation and what they will specifically measure and analyze as they look to improve your processes.

Here's what you should look for:

• Understanding of the difference between intrinsic metrics (evaluating individual components) and extrinsic metrics (evaluating end-to-end task performance)
• Awareness of business-focused data points like latency, throughput, cost efficiency, and error rates
• Insight into user-centered metrics such as satisfaction, clarity, or task-completion rate (especially for chatbots)
• Ability to explain trade-offs between different evaluation methods
• Experience setting up monitoring and continuous evaluation pipelines
• Evidence that they regularly perform error analysis and iterate based on patterns in the data
• Understanding that evaluation must reflect real-world use cases, not just academic benchmarks

Tell us about a time when you significantly increased the performance of an NLP system

While the above questions are more theoretical in nature, this question gives the candidate a chance to flex their muscles and share one of their greatest success stories. As the interviewer, you'll get to hear about their real-world experience in NLP engineering, which gives you a chance to consider the value they could bring to your organization or team.

Here's what you should look for:

• A clear, structured explanation of the problem the candidate faced and why it mattered
• Specific actions they took to diagnose issues (error analysis, data inspection, model evaluation, and pipeline debugging)
• Concrete techniques used to improve performance (e.g., better preprocessing, hyperparameter tuning, model upgrades, data augmentation, or architecture changes)
• Quantifiable outcomes such as improved accuracy, reduced latency, better F1 score, lower error rate, or higher user satisfaction
• Evidence of practical engineering skills: deploying updates, monitoring results, and collaborating with teams
• Ability to communicate complex changes in simple, understandable terms and text data
• Reflection on what they learned and how it informs their current engineering approach
• Signs of ownership, initiative, and resourcefulness

Tools and libraries

These questions will help you understand how candidates use various resources and tools to improve their work and enhance their learning. If you interview several candidates, you can develop a robust list of resources for future use simply by learning more about what others in the field are using.

How do you use NLTK and spaCY in NLP tasks?

We've already discussed the natural language toolkit (NLTK) above. spaCy is an open-source library that helps NLP and machine learning engineers do real work with a simple and easy-to-install library and a variety of plugins. Learning how familiar a candidate is with each of these tools will give you a greater understanding of how they could enhance their effectiveness by using these resources. 

Here's what you should look for:

• Understanding of the strengths and limitations of NLTK versus spaCy
• Ability to describe specific tasks they've completed with each tool (e.g., tokenization, POS tagging, NER, dependency parsing, or text classification)
• Familiarity with spaCy's pipeline architecture, models, and plugin ecosystem
• Awareness of when to choose spaCy over NLTK for performance, scale, or deployment needs
• Insight into how they handle custom models, training, or fine-tuning within spaCy
• Experience integrating these libraries into larger machine learning workflows or data pipelines
• Understanding of how these tools interact with others
• Practical examples demonstrating efficiency gains or accuracy improvements using these libraries

What open-source contributions have you made to the NLP community?

This question goes beyond the first question about familiarity with networks and considers what contributions they have made to open-source databases in the past. This will reveal both their proficiency in improving code that others have built, as well as their desire to share their knowledge and insight with others.

Here's what you should look for:

• Evidence of genuine engagement with the NLP community through repositories, libraries, datasets, or documentation
• Quality of contributions, such as bug fixes, feature additions, model implementations, performance improvements, or dataset curation
• Familiarity with collaborative development practices (pull requests, code reviews, version control, and issue tracking)
• Ability to explain the purpose and impact of their contributions, not just list them
• Understanding of open-source standards such as reproducibility, licensing, testing, and documentation
• Willingness to share knowledge through tutorials, examples, or guides

What other tools and resources do you find most helpful?

This question will help you see how the candidate includes other resources in their work while also giving you a broader knowledge base of what tools exist to help with NLP work. You'll better understand how they are working to broaden their knowledge and sharpen their skills by hearing about what books, websites, blogs, and other avenues they are using to enhance their abilities.

Here's what you should look for:

• Awareness of widely used NLP tools, libraries, and platforms beyond the basics
• Ability to justify why certain tools or resources improve workflow, accuracy, or productivity
• Signs of continuous learning through books, courses, research papers, blogs, or community forums
• Familiarity with modern frameworks (e.g., Hugging Face, PyTorch, TensorFlow) and supporting utilities

Future trends and challenges

The past few years have represented major growth and development in the field of NLP, and the landscape should continue to shift in the coming years as well. Before you wrap up the interview, take some time to hear each candidate's thoughts on the industry's future.

What's your take on the future of GPT models?

Generative pre-trained transformers (GPT) models, such as the popular ChatGPT, have impacted the world of AI. Improvements continue to take shape and enhance the capabilities of what this form of AI can do.

As you hear each candidate's thoughts on how these models could continue to deepen and develop, you'll get a sense of what they think could be possible in the field as well as what role they think they could play in these ongoing advancements.

Here's what you should look for:

• Insight into emerging trends such as multimodal models, efficiency improvements, and domain-specific fine-tuning
• Ability to articulate practical implications for NLP tasks and industry applications
• Awareness of ethical considerations, bias, and responsible deployment
• Forward-thinking perspective that shows curiosity and strategic thinking

How do you see the NLP landscape evolving?

This question will give you a chance to hear about how the candidate sees the field changing in the near or distant future. This may impact the way that they work, or it could simply reflect their beliefs, hopes, or expectations for future growth or adaptation in the industry.

Here's what you should look for:

• Awareness of major trends such as transformer dominance, multimodal models, and real-time NLP applications
• Understanding of shifts in data privacy, model efficiency, and deployment practices
• Ability to connect future trends to practical impacts on engineering work
• Thoughtful, grounded predictions rather than vague speculation
• Signs that the candidate stays informed through research, news, and industry developments

How different could AI technology look in 10 years?

While impossible to predict with full accuracy, this question sheds light on what the candidate sees as the future of the field. It will also give you a sense of whether they are generally optimistic about NLP and machine learning or if they are somewhat cynical or cautious about the future.

Here's what you should look for:

• Ability to think long-term about advancements in AI capabilities, efficiency, and integration
• Awareness of potential breakthroughs (e.g., better reasoning, personalization, autonomy, or multimodal understanding)
• Consideration of risks such as bias, misuse, regulation, and ethical constraints
• Insight into how future shifts might influence their work, skills, or approach to problem-solving

Find talented NLP engineers on Upwork

Throughout this article, we've covered several potential interview questions as well as the reasoning behind bringing the topics up with NLP engineers. The steps in this article can help create productive conversations with candidates in a way that helps you make decisions about who might be the best fit to join your team.

As you look for candidates to interview, you'll find some of the best NLP engineers and data scientists worldwide on Upwork. With Upwork, you'll be one step closer to taking your team's NLP expertise and data science capabilities to the next level.

‍

‍Upwork does not control, operate, or sponsor the tools or services discussed in this article, which are only provided as potential options. Each reader and company should take the time to adequately analyze and determine the tools or services that would best fit their specific needs and situation.