The Role of an NLP Developer

Key takeaways

NLP developers create and refine the language systems that power artificial intelligence (AI) systems. 

• The average annual base salary for an NLP developer in the US is $133,000, according to Glassdoor [1]. 

• Essential technical skills for an NLP developer include a strong foundation in programming languages, a solid understanding of machine learning algorithms, plus familiarity with Python frameworks and libraries.

• You can pursue a degree in computer science or a related field to obtain the foundational knowledge needed to succeed in the NLP developer role.

Take a closer look at what NLP developers do, including the skills and qualifications needed to become one. Afterward, if you’re ready to learn how to build and train a neural network, enroll in Machine Learning Specialization. Beginner-friendly, this program also covers best practices for machine learning development.

NLP developer job definition and role

Natural language programming (NLP) developers perform a variety of tasks in a number of roles, depending on where they work. 

What is an NLP developer?

NLP developers work to create and refine the language systems that power modern artificial intelligence (AI). In this role, you design AI with textual and speech recognition capabilities, define learning data parameters, and help develop the right algorithms for a business’s particular NLP project. 

Skills and qualifications

To become an NLP engineer, you’ll need to acquire a variety of skills and qualifications. These include a strong foundation in programming languages and a solid understanding of machine learning algorithms. 

Essential skills

NLP is a field that combines rules-based computational linguistics with the disciplines of: 

• Deep learning

• Machine learning

• Statistical modeling

As such, an NLP developer’s skills must be fairly wide-ranging. These skills encompass proficiency in various programming languages, such as: 

• C++

• Java

• JavaScript

• Julia

• Lisp

• Prolog

• Python

• R

Python remains more commonly used when it comes to NLP-based tasks. Many relevant NLP frameworks and libraries are either in Python or are easily compatible with it. You’ll need to be familiar with a variety of such frameworks and libraries, including: 

• Gensim

• Hugging Face

• Natural Language Toolkit (NLTK)

• PyTorch

• spaCy

• TensorFlow

Read more: 9 Best Python Libraries for Machine Learning

Is NLP part of AI or ML?

NLP is a branch of AI that uses machine learning (ML) to help computers interpret human language. It’s important to note that ML is also a subset of AI. Amazon’s Alexa and Apple’s Siri are practical applications of NLP. 

Educational background

While the precise educational requirements for an NLP developer vary by company, the following recommendations should generally be considered.

Relevant degrees and credentials

NLP developers usually have a bachelor’s degree in computer science or a related field. Much progress has been made toward a master’s degree in AI. Additionally, some earn certificates from various universities, online learning databases, and boot camps. Since Python is key to NLP, obtaining a certification in this programming language is a good first step. 

Tools and technologies

NLP developers utilize a variety of sophisticated tools and technologies. Be sure you become familiar with the following.

Common tools used

You’ll find NLP technology powering a variety of common devices and applications, such as:

• Chatbots

• Digital assistants

• Translation apps

• Transcription programs

NLP operates differently depending on its use case. Regardless of precisely what it is your NLP does, as an NLP developer, you’ll need to be familiar with such tools and text processing techniques as: 

• Coreference resolution: Determines when two different words identify one semantic entity, such as when a pronoun refers to an antecedent proper name.

• Lemmatization: Chops the endings off of words to group them by dictionary root (or lemma) and meaning. 

• Named entity recognition: Named entity recognition extracts words from their place in a sentence and sorts them into such categories as “cities”, “people”, or “seasons”. 

• Part-of-speech tagging: Describes and categorizes words based on their semantic values, i.e., as nouns, verbs, adjectives, adverbs, etc. 

• Sentiment analysis: Determines whether the customer-composed text is positive, negative, or neutral in overall tone.

• Stemming: Removes the endings of words more simplistically than lemmatization, reducing them to sometimes ambiguous non-dictionary stems. 

• Tokenization: Tokenization segments text into small portions, or tokens, to analyze individually, often without punctuation. 

• Word sense disambiguation: Determines the correct meaning of a word that, based on its placement in a sentence, could mean more than one thing.

Responsibilities and tasks

Your day as an NLP developer can vary, but it will likely follow the outline below.

Day-to-day activities

NLP developers build language models that power chatbots, digital personal assistants, GPS systems, and more. However, they don’t do all of this alone. 

Your day-to-day activities as an NLP developer will likely involve interaction with a variety of professionals, such as: 

• Data scientists

• Engineers of various types

• Front-end developers

Since NLP relies so heavily on data, your relationship with data scientists may be significant. Data scientists analyze data to assist in making major business decisions. To communicate fluently with them, it may be a good idea for you to familiarize yourself with their discourse. Understand essential data science concepts such as: 

• Data cleaning

• Data mining

• Data modeling

• Data warehousing

You’ll also likely meet, from time to time, with stakeholders who don’t necessarily possess much technical expertise. As such, in addition to your technical skills, you’ll have to develop various workplace skills that allow you to convey complex ideas to a broad audience of professionals. These skills include: 

• Adaptability

• Cross-cultural communication

• Empathy

• Leadership skills

• Presentation and public speaking

• Problem-solving

• Time management

• User-centric thinking

• Willingness to learn new things

• Written and spoken communication

NLP developer salary expectations

You can use the information below to gauge the salary possibilities as an NLP developer.

Average salaries

The median total pay for an NLP developer is $156,000 [1]. This figure includes base salary and additional pay, which may represent profit-sharing, commissions, bonuses, or other compensation. Salary varies depending on experience, location, and company. 

Fortunately, NLP developers are in high demand in a variety of industries and sectors, including: 

• Defense

• Finance

• Government

• Health care

• Marketing

Career path and advancement opportunities

When deciding how best to proceed, it’s worth studying an NLP developer's career path and growth potential.

Potential growth

The job outlook for NLP developers is highly favorable. The field of computer and information research science (under which umbrella NLP developers fall) is likely to grow 20 percent between 2024 and 2034 [2]. This is much higher than average. 

Industry demand and trends

Industry demand for NLP developers remains high and is expected to grow further. Below is more information regarding specific companies. 

Current job market

The number and variety of major companies hiring NLP developers speak to this growth. These companies include: 

• Apple

• Amazon

• Bank of America

• Capital One

• CVS Health

• Etsy

• US Food and Drug Administration

• Google

• IBM

• Lockheed Martin

• Microsoft

• National Institutes of Health

• PayPal

• Spotify

Common challenges

Despite the in-demand nature of their career, NLP developers may still encounter hurdles to overcome. The points below might be worth your consideration.

Obstacles faced

As in any field, challenges exist when it comes to NLP development. These include: 

• Bias: NLP models trained on massive unstructured internet data sets can inadvertently pick up factually incorrect, socially biased data. While generative AI models strive to produce unbiased content, NLP developers must continuously refine their models, right-size their data architectures, and, occasionally, manually curate and adjust training data. 

• Environmental impact: Large language models (LLMs), including those that power NLP models, require a great deal of energy to operate. The carbon footprint resulting from LLM usage is likely significant. They may consume even more energy during the user phase than during training. Finding new ways to leverage renewable energy may be the next frontier of NLP development. 

• Explainability: Known as the “black box” problem in AI, explainability continues to be challenging for NLP developers and AI professionals of all types. Simply put, they don’t yet fully understand why an AI model generates the output it does. As such, even carefully curated NLP and AI programs can arrive at often confounding conclusions.

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