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How to become a natural language processing engineer

Explore the education, experience and skills needed to excel in the demanding yet rewarding field of NLP engineering, including expertise in linguistics, math and programming.

Deriving meaning from and conveying information through natural language is a fundamental human ability. As computers advance, they are beginning to understand and share natural language with humans thanks to the field of natural language processing.

The potential for NLP is vast, as it enables computers to comprehend human language and interpret the subtle spoken cues, phrases and inflections that convey mental and emotional states. Although NLP has been around since the 1950s, AI chatbots such as OpenAI's ChatGPT and Google's Gemini are achieving new levels of success when it comes to natural language platforms.

NLP is a complex field that requires strong skills in mathematics, programming and linguistics. Technologists seeking a career in NLP can explore the challenging role of an NLP engineer, which involves a wide range of responsibilities and a comprehensive skill set.

What is natural language processing?

NLP is a specialization within the broader field of AI. NLP enables computer systems to decipher and understand human language, whether written text or spoken words.

NLP is a multifaceted field that requires comprehensive knowledge of linguistics to understand the concepts and functions of natural language. It also necessitates strong mathematics and machine learning skills to break down, analyze and extract details from text and speech. Finally, NLP engineers need excellent programming skills to create the models and routines used to build NLP platforms for businesses.

NLP gathers information and translates natural language into meaningful data or actionable instructions for the computer. Although AI tools and systems often respond to human queries through text, NLP can also pair with text-to-speech technologies to present AI responses as natural speech output.

While NLP is a core aspect of increasingly popular generative AI chatbots, it has a wide range of other language-driven use cases, including text analysis and categorization, language translation, grammar checking and spam detection.

Uses of NLP include machine translation, information retrieval, sentiment analysis, information extraction and question answering.
While generative AI is an important subfield of NLP, the discipline also encompasses areas like translation and sentiment analysis.

What is a natural language processing engineer?

An NLP engineer is a high-level technical professional responsible for developing, training and maintaining an organization's NLP projects and integrating NLP into its AI systems.

NLP engineers specialize in creating user interfaces (UIs) that enable AI systems to gather useful and actionable insights from natural-language data. In effect, NLP engineers build the bridge between human language and computer understanding.

While individual NLP engineers will have varying responsibilities, depending on the goals of their business and projects, they typically support a range of core tasks:

  • Model design. NLP engineers design the algorithms and ML models integrated into NLP systems. These models vary depending on their purpose; for example, sentiment analysis models differ from those used for question answering or generative AI prompt interfaces.
  • Software development. NLP engineers develop and test NLP code, often collaborating with software developers and QA teams. They focus on coding and testing models and algorithms, culminating in the deployment of a working NLP platform.
  • Training and assessment. NLP engineers use data to train an NLP platform, often working with the data science team. They support deployment with various metrics and KPI monitoring tools to gauge the platform's accuracy, performance and effectiveness in understanding natural-language input.
  • Integrations. In many cases, the NLP platform is not useful by itself and is instead intended as a UI front end for a larger AI system. NLP engineers integrate models and NLP platforms into their intended AI applications, such as chatbots or helpdesk systems.
  • Ongoing improvement. NLP development is cyclical. Engineers monitor feedback over time to refine models and optimize code. They also further adapt NLP platforms to changing language, such as new slang or localizations, and user needs, such as new features and functions.

NLP engineers are almost always part of a broader AI team, collaborating closely with other technical and business professionals, such as the following:

  • AI project managers and platform stakeholders.
  • Other AI system or platform teams, such as computer vision teams that complement NLP and text-to-speech teams.
  • Software developers and testers, who help build and maintain the NLP and AI software.
  • Data scientists and data engineers, who create and supply quality data for ML training.

NLP engineer skills

An NLP engineer needs extensive knowledge of linguistics to understand language theory; ML to understand the methodologies and algorithms needed to process language elements; and programming skills to translate that knowledge into software code within an NLP platform.

Successful candidates for NLP roles must possess several critical skill sets:

  • Linguistics. Because language is the core of NLP, a keen understanding of language is essential, including structure, syntax and semantics. While it's possible to start an NLP engineering career without linguistics, expertise in that field will strengthen long-term career prospects.
  • Machine learning. Expertise in ML algorithms, models and advanced techniques -- such as deep learning and recurrent neural networks -- is needed to translate and process language into elements that an AI system can understand. Experience with established ML tools such as PyTorch, Keras and scikit-learn is also beneficial.
  • Programming. While NLP engineers might not perform extensive programming, the ability to translate developed NLP models into executable code is essential. Strong knowledge of software architecture, design, comprehensive testing and quality coding in languages like Python, Go, R, Java and C++ is vital.
  • Data science. Models have little value without data. NLP engineers benefit from a knowledge of data science basics, including statistics, data structures, data analysis and data visualization. NLP engineers often work closely with data scientists to curate training data and conduct baseline performance measurements.
  • Problem-solving. Language is complex and highly nuanced. NLP engineers must use their knowledge of linguistics, ML models, programming and data science to translate business tasks and objectives into a well-conceived NLP platform. Every project is different and poses varied challenges for the NLP engineering team.
  • Interpersonal skills. NLP engineers cannot work alone. NLP projects involve programmers, data scientists, operations teams, project stakeholders -- such as project managers and department heads -- and users. This makes soft skills like communication and collaboration important.

How to become an NLP engineer

The path to becoming an NLP engineer can be long and demanding, requiring competence in several broad skill sets. The road is further complicated by variations in business needs and project goals. Taken together, a career as an NLP engineer can take years to build. To get started, consider these strategic steps.

Start with education

NLP engineers require some amount of formal education. A degree in one of these key areas is a great place to start:

  • Linguistics to understand the theory and construction of human language.
  • Mathematics, including linear algebra, probability and statistics and calculus, to understand the algorithms used in NLP.
  • Programming to translate linguistic theory and mathematical abstracts into workable ML models and other code for NLP projects.

Of these areas, linguistics and mathematics are most likely to warrant a degree early on. While a programming degree is also beneficial, there are ample opportunities for self-study and continuing education in software development, which are harder to find for linguistics and mathematics.

Focus on machine learning

With fundamental education in place -- or as part of an extended basic education effort -- a fledgling NLP practitioner can divert their energies to additional studies important to NLP implementations, such as the following:

  • Data studies offer a solid understanding of data structures and algorithms. Related concepts such as graphs, hash tables, searching and optimization are critical to designing and implementing effective NLP platforms.
  • Machine learning studies are another integral skill set for NLP engineers, including a solid understanding of model development, training, optimization and maintenance.

Gain experience

Aspiring NLP engineers can join a business as part of a software engineering team involved in entry-level NLP projects. Depending on the stage of your career, look for internships, work-study arrangements, or paid part- or full-time positions to gain insight into how NLP projects operate. These opportunities to contribute, coupled with educational background and other practical experience, can be pivotal to future career choices and opportunities.

Focus on NLP

Once involved with practical AI and NLP-related projects, maturing NLP engineers can further specialize to hone their knowledge and practical skills. For example, an NLP engineer could engage in deeper study of concepts such as tokenization, named entity recognition or sentiment analysis. NLP practical capabilities can be developed by acquiring proficiency with NLP tools, libraries and frameworks, such as spaCy, Gensim, TensorFlow and PyTorch.

Seek more challenging experience

With a growing resume of solid education and practical experience from NLP project work, NLP engineers can seek engagement with more complex projects -- for example, moving from simple sentiment analysis to more complex chat systems. This might occur within the same business or with other organizations offering new, more challenging opportunities.

Seek more education

Learning never stops. NLP engineers should continually pursue new courses, read research, attend conferences and webinars, and even consider advanced education, such as a master's degree or a doctorate in linguistics or computer science. With more education and practical experience, NLP engineers can then seek more challenging opportunities, repeating the cycle of working, learning and growing throughout their career.

Stephen J. Bigelow, senior technology editor at TechTarget, has more than 20 years of technical writing experience in the PC and technology industry.

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