How to Become an NLP Engineer

5 min read

Have you ever asked Alexa to recommend a specific recipe or typed “best restaurants san diego” into Google only to really mean “What are the best restaurants in San Diego?” Do you use the autocorrect feature when writing emails or allow your smartphone to autocomplete a text message?

If you’ve answered “yes” to any of those scenarios (and there’s a good chance you have), you can thank natural language processing (NLP).

TechRepublic defines NLP this way: When you communicate with your computer, tablet, phone, or smart assistant (either through speaking or a chatbox), and your electronic device understands what you’re saying — that’s due to natural language processing.

So, in addition to smart assistants, search engine results, and autocomplete and autocorrect features, how else is natural language processing used?

According to Tableau, here are some additional applications:

  • Email filters: For example, Gmail automatically sorts new emails into three categories: primary, social and promotions.
  • Language translation: Online translation programs have come a long way to produce more grammatically correct results.
  • Text analytics: Otherwise known as sentiment analysis, a tool using NLP can review social media comments or product/service reviews to determine what’s being said and how people feel about a particular brand.

Now that we’ve explored the prevalent concept of natural language processing, let’s talk about the professionals who make it all possible — NLP engineers.

What is a Natural Language Processing Engineer?

The exact responsibilities will depend on the position and industry, but according to Freelancermap, an NLP engineer will typically:

  • Design natural language processing systems
  • Deal with speech patterns and AI speech recognition
  • Program a technology’s ability to process and analyze natural language data
  • Define datasets for language learning
  • Find and implement the right algorithms and tools for NLP projects
  • Analyze NLP results and refine models as needed
  • Keep up with the latest in ML and AI

What is an Average Day for an NLP Engineer?

While we just outlined the typical NLP engineer responsibilities, it’s important to point out that an “average day” for each NLP will differ based on the industry and organization. In this day-in-the-life article from Acclaro, an NLP engineer talks about how his day “has its fill of interruptions — extremely good interruptions, like emails about new projects and ideas” that can impact his company’s growth.

If you’re an NLP engineer, expect to dive deep into data, analysis, predictive models and algorithms. Depending on the company or organization, you may likely work with other engineers, data scientists, designers and front-end developers. You may find yourself in a fair amount of meetings and will probably have to relay technical information to a non-technical audience through reports or presentations.

Paths to Become an NLP Engineer (Degree vs. Non-degree)

Many NLP engineers come from an academic background. An associate or bachelor’s degree in engineering, data science or computer science is typically preferred. If a bachelor’s degree isn’t mandatory, a certain amount of experience may be required (including the completion of some NLP courses).

Earning a certificate is also a good way to gain experience, according to Freelancermap. In some cases, a master’s degree or Ph.D. is required.

Many companies offer junior-level or apprentice positions or internships where not as much experience is required. This is a good way to get your foot in the door.

NLP Engineer Hard Skills

If you peruse any type of NLP engineer job posting, you’ll likely see the same types of required skills, which, according to Workable, include:

  • Experience with programming languages (Python, Java and R)
  • Statistical analysis
  • Machine learning (ML) methods
  • Text representation techniques
  • Ability to write “robust and testable code”
  • Experience with ML frameworks and libraries

Strong programming skills and previous NLP experience is often a must-have for many positions, especially senior-level ones. Government or defense contractor positions may also require a security clearance.

NLP Engineer Soft Skills

Soft skills are often desired just as much as hard skills because employers are seeking NLP professionals who are team players, problem solvers and excellent communicators.

Additional soft skills, courtesy of Indeed, include:

  • Creativity
  • Self-motivation
  • Dependability
  • Positivity
  • Conflict management
  • Collaboration
  • Critical thinking
  • Discipline
  • Organization
  • Time management
  • Leadership

Education Requirements for an NLP Engineer

So, what kind of degree is typically preferred to become an NLP engineer? The answer is an associate or bachelor’s degree in a related field, such as engineering, data science, or computer science. A master’s degree in a related subject such as data science or artificial intelligence or a Ph.D. with a focus in NLP is likely to be preferred or required for high-level positions. A certain amount of professional experience may also be a sufficient substitute for an advanced degree, depending on the company.

NLP Engineer vs. Other Similar Job Titles

A search for “natural language processing engineer” or “NLP engineer” on sites such as LinkedIn and Indeed will turn up thousands of search results. In many cases, “NLP” is not in the name of a job title, but the positions listed include natural language processing-related job responsibilities. These titles include:

  • Software Engineer
  • Machine Learning Engineer
  • Artificial Intelligence Engineer
  • Language Engineer
  • Natural Language Understanding Engineer
  • NLP Data Scientist
  • NLP Research Scientist/Engineer
  • Research Engineer
  • Research and Development Engineer
  • Data Science Solutions Engineer
  • Data Scientist

How to Move up the NLP Engineer Ranks

According to Hired, starting as a junior-level NLP engineer is a good way to gain experience and become proficient with the required tools and methods you’ll need to eventually work your way up the ladder. You will also likely work with other engineers, which can be extremely valuable.

Just like with any position at any company, you can move up the ranks by proving that you’re extremely valuable to the company or organization. Attaining a senior-level position may take time, experience and advanced education. A master’s degree or Ph.D. in a related field can provide more experience and set you apart from other candidates, especially if you’re eyeing a more senior-level position — and in some cases, it’s required.

Companies by Industry Hiring NLP Engineers

NLP engineers are the most sought after in Seattle, New York, Boston, London and the San Francisco Bay Area, according to the job resource Hired. But the good news is that most industries — as outlined below — have job postings for all types of positions. Based on recent job searches conducted on LinkedIn and Indeed, here is a breakdown of companies by industry that have listed NLP engineer-related positions:


  • Amazon
  • Etsy
  • Nike
  • Wayfair
  • Under Armour
  • Dick’s Sporting Goods
  • Patagonia
  • Sephora

Information Technology & Services

  • Microsoft
  • IBM
  • Apple
  • Zoom
  • Google


  • Fidelity Investments
  • Capital One
  • Bank of America
  • Farmers Insurance
  • PayPal

Entertainment/Sports/Social Media

  • Spotify
  • Facebook
  • Warner Bros. Entertainment
  • Kansas City Chiefs
  • TikTok
  • Instagram


  • Massachusetts Institute of Technology
  • Yale University
  • Pearson

Defense & Space

  • Booz Allen Hamilton
  • Lockheed Martin

Marketing & Advertising

  • ZipRecruiter
  • Zillow


  • Johnson & Johnson
  • CVS Health
  • Anthem, Inc.
  • The National Institutes of Health


  • Federal Reserve Board
  • Food and Drug Administration
  • National Security Agency

Visit LinkedIn and Indeed for the most up-to-date search results.

NLP Engineer Career Outlook

The artificial intelligence industry as a whole is rapidly evolving — with hundreds of new job postings every day around the world. In fact, a recent LinkedIn search revealed more than 500 job postings in 24 hours for “natural language processing engineer” positions (and 3,000-plus postings for AI positions within the last 24 hours). While this number will change daily, it’s safe to say the outlook for NLP engineer positions is extremely promising.

On a broader level, the projected growth for Computer and Information Research Scientists is projected to grow 22% from 2020 to 2030, which is much faster than the average for other occupations. This category includes jobs that invent and design new approaches to technology and find uses for existing technology.

NLP Engineer Salaries

The salary ranges for an NLP engineer will vary depending on the company, position level, your experience and where the role is physically located, but ZipRecruiter provides an average salary of $134,096. According to Hired, the average salary for an NLP engineer in Seattle is $156,680 and $146,142 in Washington, D.C.

NLP Engineer Career FAQs

What is a natural language processing engineer?

An NLP engineer is someone who works with speech patterns and AI speech recognition so that when people communicate with technology, the electronic device “understands” what they are saying.

What are some of the top NLP blogs and resources?

We recommend Github’s curated list of NLP content, Analytics Vidhya, and IBM’s comprehensive blog.

How important is a master’s degree in seeking a career in natural learning processing?

It can be extremely important. In fact, an advanced degree may be required in many cases, and if it’s not, having one will illustrate your dedication to the field and may set you apart from other candidates.

What is the average NLP engineer salary?

It will depend on a number of factors, but ZipRecruiter lists the average salary for an NLP engineer at $134,096.

This career information was brought to you by the University of San Diego’s Master of Science in Applied Artificial Intelligence. This program is designed to equip graduates with the foundation, principals, technical knowledge and skills needed to apply powerful, modern AI techniques to real-world applications.

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