What Does an NLP Engineer Do?

Natural language processing (NLP) engineers focus their efforts on creating algorithms, programs, and applications that help computers process human language for various purposes. The type of programs you create as an NLP engineer depends on your industry. You can find NLP careers in various industries, from retail to financial services and beyond.

To work as an NLP engineer, you’ll need to acquire and build a specified set of skills. Common NLP engineer skills include coding, statistical analysis, programming languages, machine learning methodology and frameworks, and techniques in text representation. You can acquire these skills through undergraduate or graduate degree programs as well as certifications, courses, and bootcamps. Experience is another important step on the pathway to becoming an NLP engineer. It’s common for NLP engineers to work as junior NLP engineers before stepping into their roles as NLP engineers.

The job outlook for NLP engineers and related careers exceeds the growth rate of other occupations in the US through 2025 [1]. The demand for these specialized engineers stands to grow and expand as the use of virtual assistants, chatbots, and other forms of speech recognition systems continues to rise. Industries ranging from health care to retail depend on natural language processing systems to meet the demands of consumers and manage big data. For example, in health care, practitioners can more efficiently and accurately record medical notes and access them quickly when needed through natural language processing tools and AI.

What is NLP engineering?

NLP engineering is a branch of artificial intelligence (AI) that focuses on developing systems to process, interpret, and generate human language in spoken and written forms. Some examples of NLP engineering include voice-activated smart assistants, chatbots, online language translation programs, autocorrect features, and search engine results. NLP is a helpful tool for organizations with a lot of data to manage.

The main applications or uses for natural language processing include:

• Chatbots and virtual assistants: Allows organizations to communicate with customers using automated conversations between the individual and the chatbot or virtual assistant 

• Question answering: Automatically responds to individuals’ queries, with or without context—often used to extract specific information from larger portions of data

• Text classification: Categorizes and predicts text for various purposes—for example, a spam filter on an email or categorizing customer support requests

• Searching: Interprets and classifies search queries on search engines 

• Text summarization: Condenses, or summarizes, long text to better capture and convey the main idea 

• Sentiment analysis: Determines the emotional tone of a message—companies may use this application of NLP as a tool via email correspondences, social media comments, and customer support chatbots

NLP engineers, sometimes called machine learning engineers, are the individuals who work in this field, designing, programming, and creating the models that computers use to process and better understand human language. 

What does an NLP engineer do?

NLP engineers design natural language processing systems that computers use to understand human language. These engineers write the coding necessary for computer systems to process human language and make sense of it for numerous purposes.

Depending on your exact role and industry, as an NLP engineer, you will likely work with AI speech recognition technology and machine learning concepts. For example, an NLP engineer uses algorithms to teach machines to learn human speech in a way that also allows the machine to learn more over time. In other words, the machine gets better at recognizing language as it’s used.

NLP engineer tasks and responsibilities

Relying on a mix of machine learning and AI speech recognition, NLP engineers develop and design programs that computers use to process and understand spoken and written human language. Referred to as text representation techniques, NLP engineers turn words and phrases commonly used in the human language into code that computers can use. 

NLP engineers typically work full-time schedules and often collaborate with other engineers. Most of the work of an engineer is done online and with computer systems. 

Common responsibilities include: 

• Develop NLP systems according to specific requirements. NLP engineers create computers that understand human language and can communicate with humans. To do this, they create software programs that allow computers to process and interact with spoken or written language. NLP engineers develop code solutions for a variety of NLP tasks, including natural language generation, sentiment analysis, named entity recognition, and speech and text classification.

• Complete data preparation and preprocessing: NLP engineers write code to clean, preprocess, and transform raw text data into a format suitable for modeling. The process might involve stemming, tokenizing, and removing stop words.

• Identify and implement the right algorithms, frameworks, and tools for NLP tasks: NP engineers use specific algorithms and NLP models to extract information from human language and translate it into code that computers can understand and use. The type and functionality of the NLP system vary depending on its application. 

• Perform statistical analysis on NLP models: An important responsibility of an NLP engineer involves performing statistical analysis of the NLP models they choose or design and then refining those models as needed based on feedback and tests. NLP engineers use various metrics to assess NLP models, which include F1-score, accuracy, human evaluation, and more. 

NLP engineer skills

It’s important that NLP engineers have specific technical and interpersonal skills that allow them to do everything necessary to create systems that computers can use to understand and interact with human language. These skills include a strong knowledge of mathematics and statistics, perseverance, and proficiency in AI and machine learning concepts. 

Technical skills

Some common technical skills to hold as an NLP engineer include:

• Machine learning methodology and frameworks 

• Statistical analysis 

• Deep understanding of a variety of programming languages 

• Coding in multiple languages 

• Text representation techniques 

• Data modeling 

• Engineering structures 

Workplace skills

Some common interpersonal workplace skills to hold as an NLP engineer include: 

• Critical thinking 

• Analytical thinking

• Intrinsic motivation 

• Organization 

• Time management 

• Good communication 

• Team player 

• Desire to learn about emerging technologies 

NLP engineer salary and job outlook

As of January 2025, NLP engineers working in the US earn an average of $123,186 a year, according to Glassdoor [2]. Factors such as geographical location, years of experience, level of education, additional certifications, industry, and company size will likely impact your earnings as an NLP engineer. For example, you might earn more living and working in a city where many larger tech companies are based. Zippia reports many of the highest-paying employers of NLP engineers are in California cities, where many of the nation’s largest tech companies are based [3]. You might also find an uptick in salary if you have advanced degrees and additional industry-specific certifications. 

The US Bureau of Labor Statistics expects the field of computer and information research scientists, which includes the role of NLP engineer, to grow 26 percent from 2022 to 2032 [4]. This growth rate far surpasses many other occupations, making the career of NLP engineer a fast-growing and robust profession in computer and information research. The increased and rising demand for engineers working in this field is likely due to an enhanced demand for information processing and a greater volume of data management.

NLP engineer career path

Most NLP engineers work as junior engineers for a while to gain experience and build their skills. Junior engineers often work on a team with other junior engineers. Some individuals work as software engineers to gain experience before working in the field of NLP as a junior engineer. Among the best ways to progress in your career path as an NLP engineer and beyond are through experience and skill-building through certifications. 

After you’ve worked as an NLP engineer for at least five years, you may pursue more advanced positions in the field of machine learning. Some of these advanced-level jobs in machine learning and natural language processing include [5]: 

Some NLP engineers may also transition into similar positions in other fields outside of machine learning and AI, such as software engineer or data scientist.  

The salary breakdown by NLP job title as you progress through your career path in this field is [2]: 

Education and training

Most employers require a bachelor’s degree in computer science or a related field to become an NLP engineer. NLP engineers who want to advance in their careers may choose to pursue an advanced degree in AI.

In addition to education, training in specific tools, programming languages, and concepts used in machine learning is critically important. A prospective NLP engineer may enroll in certifications, work as a junior NLP engineer or software engineer to gain experience, or work in computer programming to learn more about coding and other foundational skills necessary for jobs in AI. 

Common Specializations, Professional Certificates, and courses a potential or current NLP engineer may enroll in include: 

• DeepLearning.AI's Natural Language Processing Specialization

• Microsoft's Natural Language Processing in Microsoft Azure

• DeepLearning.AI's Natural Language Processing in TensorFlow

• IBM's IBM Applied AI Professional Certificate

• DeepLearning.AI's Natural Language Processing With Classification and Vector Spaces

• University of Michigan's Applied Data Science With Python Specialization

Getting started with Coursera

Learn more about the world of machine learning with Generative AI With Large Language Models on Coursera. In this course, intended for individuals with prior knowledge of machine learning concepts, you will have the opportunity to learn how generative AI works, along with real-world applications of NLP today. As a prospective NLP engineer, you can also explore the profession and prepare for a career in the field of AI as an NLP engineer through online courses.