How to Become a Natural Language Processing Engineer
How to Become a Natural Language Processing Engineer
How to Become a Natural Language Processing Engineer

Natural language processing (NLP) enables people to use everyday language to communicate directly with the digital assistants available on their mobile phones, car dashboards, and TV remote controls, as well as language education and translation apps, chatbots, and transcription services.

A branch of artificial intelligence (AI), NLP translates the complexities of human text and speech into the ones and zeros of computer language. The results can be satisfying when NLP does its job well, but frustrating when there’s a disconnect between person and computer.

An example is Amazon’s NLP-enabled assistant, Alexa. It can answer queries about the weather forecast, lay out a daily schedule, provide the answer to that tricky trivia question, and more.

NLP engineers do the coding that allows people and computers to converse. They deploy skills in linguistics, mathematics, computer science, statistics, and AI that they cultivated and strengthened by earning an advanced degree in a relevant field.

A person holding a cellphone speaks to a digital assistant.

What Does an NLP Engineer Do?

People talk to each other in natural language. Most people who are fluent in a language can understand unfamiliar words in context and get jokes or sarcasm in that language. But computers have a hard time processing the tone or hidden meaning of a remark.

That’s where NLP engineers step in. They translate the words people use into mathematical models that computers can understand. The models convert definitions, context, syntax, and other relevant language variables into coded instructions computers can process.

In building mathematical models, NLP engineers must be aware of how the language works, its quirks, and how a wide range of people might pose a question using different words, different word orders, or unusual syntax.

NLP engineers also bring to bear elements of artificial intelligence, including pattern recognition and machine learning, when building models.

NLP Tools

NLP engineers use different tools to sort words and phrases into small units that can be more easily translated into a programming language. One tool, language modeling, employs probabilistic approaches to determine which words appear more frequently in conjunction with certain other words. There are different language modeling techniques for different situations. For example, customer service bots that interact with customers use a different technique than tools that draw coherent information from unstructured data.

Another NLP tool is part-of-speech tagging, also known as grammatical tagging. It’s the process of figuring out a word’s part of speech — verb, noun, pronoun, adjective — based on its use and context. In identifying parts of speech, tagging helps computers understand what a sentence means.

NLP engineers also use the contextual tool of sense disambiguation to help computers determine which of multiple meanings is correct. For example, word sense disambiguation could help a computer distinguish between two meanings of the word “grill,” as in, “She cooked a hamburger on the grill,” as opposed to, “The police are going to grill the suspect for information.” One method identifies words by their dictionary meaning, while another learns from machine learning algorithms.

Another technique, named entity recognition (NEM), identifies words or phrases in text that could be names of places, groups, addresses, and the like.

Statistical Modeling Methods

To build language models, NLP engineers deploy several kinds of statistical modeling. These include:

  • N-gram. An n-gram is a string of words, syllables, or phonemes (n is the number of words in the string). An n-gram model is a probabilistic model that predicts the next item in the sequence. An n = 5 gram might be: “who is the star of.”
  • Unigram. A unigram (n = 1) assesses single words or phrases independently. Unigram models are usually deployed in information retrieval.
  • Bidirectional. Bidirectional models analyze text from the front of a sentence onward and from the end of a sentence back to the beginning. Every word in the sentence is used to bolster the model’s accuracy.
  • Exponential. An exponential model assigns probabilities to words appearing together through an equation that sets parameters to narrow results to the ones most likely to be correct.
  • Continuous space. The continuous space model ventures into neural networks to assign probabilities to words. Continuous space models are useful in larger data sets that use more unique words.

NLP engineers can automate many of these processes through machine learning, which uses algorithms that teach machines to learn and improve from experience without being directly commanded to do so. Digital assistants such as Alexa and Apple’s Siri get constant on-the-job training as they process multitudes of queries and commands every day.

Steps to Becoming an NLP Engineer

Continual improvements in NLP have been enabled by the convergence of technologies such as the internet, big data, machine learning, and processing power. Someone well versed in one or more of these technologies has taken a good step toward becoming an NLP engineer.


An undergraduate degree in computer science or a related subject can lead to advanced learning in a graduate program. Most NLP engineer positions require an undergraduate degree, but earning a graduate degree in a field such as artificial intelligence can open up further opportunities for employment and advancement.

Pursuing a certificate can also cultivate skills that contribute to success in a career as an NLP engineer. Maryville University’s online artificial intelligence certificates teach fundamental machine learning and AI skills and tools such as Python and AWS in as few as 15 to 18 credit hours.


NLP engineers should also know the programming languages C, C++, Python, R, and Java. They need to be familiar with MLOps, an infrastructure for automating machine language applications. MLOps tools include MLFlow and AirFlow.

Besides technical and linguistic skills, an NLP engineer needs to be familiar with the industry they’re developing applications for, as many fields rely on specialized vocabulary. They also should be aware of the conditions in the specific locations where speech recognition is used in their industry, whether it’s a quiet office or a noisy manufacturing floor.

NLP Engineer Salary

The median annual salary for NLP engineers was about $117,000 as of June 2022, according to PayScale. A number of other jobs titles tracked by PayScale that may involve NLP engineering, such as data scientist, machine learning engineer, and software engineer, had median annual salaries of more than $100,000. The median salary for machine learning engineers with NLP skills, for example, was about $116,000 a year.

Become a Leader in an Evolving Field

Natural language processing applications are becoming more common and better. Many people dictate text messages instead of typing them. Applications are common in manufacturing, gaming, agriculture, the automotive industry, and healthcare, among other industries.

Computer science professionals interested in a career as an NLP engineer should consider pursuing a specialized degree like Maryville University’s online Master of Science in Artificial Intelligence or one of its online AI certificates. Take the first brave step to a career in natural language processing with Maryville.

Recommended Reading

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Freelancermap, What Does an NLP Engineer Do?

Hired, “Career Path: How to Become an NLP Engineer”

Investopedia, Natural Language Processing (NLP)

Payscale, Natural Language Processing Engineer Salary

Payscale, Salary for Skill: Natural Language Processing

Towards Data Science, “A Learning Path to Becoming a Natural Language Processing Expert”

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