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Career Path: How to Become an NLP Engineer

When tech companies want to create sophisticated computers that can understand human language, they hire NLP Engineers to complete this task. NLP, or natural language processing, is a form of artificial intelligence (AI) and is especially useful for retrieving and analyzing unstructured data and public opinions, which is highly beneficial to many companies. The public is becoming more aware of how useful NLP devices can be and are used by many people daily, thanks to Amazon’s voice-activated assistant, Alexa and FitBit's voice-activated fitness tracker option.

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Career Path: How to Become an NLP Engineer

When tech companies want to create sophisticated computers that can understand human language, they hire NLP Engineers to complete this task. NLP, or natural language processing, is a form of artificial intelligence (AI) and is especially useful for retrieving and analyzing unstructured data and public opinions, which is highly beneficial to many companies. The public is becoming more aware of how useful NLP devices can be and are used by many people daily, thanks to Amazon’s voice-activated assistant, Alexa and FitBit's voice-activated fitness tracker option.

For those interested in becoming a professional NLP Engineer, there are many components to consider to be a success in this career.

Getting Through the Door

NLP Engineers largely deal with academic work including research, mathematics and deep learning as part of their daily work. Many have a background in computer science, data science or math, which a college degree to match. Getting an entry-level position in NLP Engineering can mean having a relevant amount of experience in certain aspects, with hiring managers likely looking for candidates with proven experience in aspects such as:

  • A thorough understanding of algebra and probability for handling commonly-used methods.
  • A grasp on linguistics, including semantics and symbolic language representations, to better understand how computational methods work.
  • An interest in data and unstructured text that will contribute to creating models and system infrastructure.

By demonstrating you have been working towards understanding the basics of NLP and putting your theories into practice with models and structures, it can show potential employers you are on the right track to making a difference in the field of NLP Engineering.

Degrees and Experience

The education aspiring NLP Engineers attain varies, but many have a degree in computer science, mathematics, or statistics. Some have a bachelor’s degree when they enter the workplace, while others complete their Ph.D., honing their research methods in an academic setting before entering the workforce.

Because NLP is gaining prominence in many industries, it is key for an aspiring NLP Engineer to figure out their area of interest and gain meaningful experience in that field to land a related job position. We are seeing NLP being used beyond the tech industry and in such places as the healthcare field to make medical records search faster and more complete, in the field of law to help with researching relevant legal documents and in financial institutions to reduce the risk of fraud. NLP Engineers may wish to work in these or other fields, so understanding how your work in NLP can be beneficial in a particular field and gaining experience there can help make your job prospects even brighter.

Working as a Junior Level NLP Engineer

NLP Engineers at the junior level have demonstrated their growing proficiency with NLP tools, methods and means. They likely work on a team with other NLP Engineers, engaging in different techniques to create practical applications for natural language processing. Their goal is to take raw language input and use linguistics algorithms to enrich the raw language, so it offers greater value. NLP Engineers daily tasks may include content categorization, topic discovery and modeling, contextual extraction, sentiment analysis, speech-to-text and text-to-speech conversions, document summarization and machine translation, all to meet their goal. Depending on the organization, a junior NLP Engineer may be responsible for overseeing an NLP team or for managing a certain aspect of NLP work to help make projects flow more efficiently.

Moving up the Ranks

NLP Engineering is a career that is ever-evolving. Though it has been around for decades, NLP itself is gaining a resurgence in popularity. This means a passionate and dedicated NLP Engineer can work their way up the career ladder by proving themselves indispensable to their company in their role.

For example, maybe you work for a banking company that is looking for better ways to serve its customers with its downloadable app and realize having voice-activated capabilities is ideal. While working on structuring the NLP components of this app, maybe you identify another voice-activated feature based on consumer data trends that you think will put this app beyond anything your company’s competitors have accomplished.

By identifying something special that can put your NLP devices above and beyond expectations, you will certainly stand out with your employers and steadily work your way up the NLP Engineering ranks.

Advance Your Career: How to Become a Senior NLP Engineer

Becoming a senior NLP Engineer takes perseverance and a continuing accumulation of skill and experience. NLP Engineers need to be technically skilled as well as understand how data and mathematics can help you engineer computers that understand and interpret human language. Working towards a senior level of their career, NLP Engineers can expect to work on more complex projects that utilize a range of skillsets. They may be tasked with overseeing NLP projects they and their colleagues work on as well as train new hires. By demonstrating your ability to successfully complete your daily tasks and be keen to take on new tasks is a way to show your employers you are ready to take on a senior NLP Engineer role.

Study the Core Fields

Studying the components related to NLP will help you become a more thorough NLP Engineer. A few of the core fields most relevant to NLP Engineering include AI, computers with artificial intelligence that simulates human intelligence processes; symbolic paradigms, following a set of rules to establish think pattern-matching; machine learning, creating devices with AI so that the device can complete a task with prompting; and stochastic paradigms, which creates conclusions drawn from statistics and probability. Having a thorough understanding of these NLP subsectors can make you a more focused and effective NLP Engineer.

Invest in Yourself

This is especially important in an evolving tech field. Taking the time to sharpen your NLP Engineering skills is sure to help you advance your career. Taking online courses and earning advanced certifications in NLP Engineering and other AI disciplines is a great way to keep you apprised of emerging technologies. Attending conferences and seminars related to deep learning technologies is also a wonderful way to stay ahead of the tech curve and can help you meet new colleagues with which to network. Investing in yourself is as an NLP Engineer will help you secure your future.

Don’t Stop at NLP Engineering

Better define your tasks and goals by studying occupations related to NLP Engineering. This can include machine learning engineers, those who build machines to have the aforementioned knowledge to complete a task; software engineers, those who design and implement software and software systems that satisfy a need; and data scientists, those who study big data and use it to tell a story that helps organizations understand their customers, competitors and operations. Opening your eyes to these fields can make you a better colleague as well as possibly interest you in a different, though related, field.

NLP Engineer Job Description

NLP Engineers create devices and systems that can understand the human language. They parcel language into shorter, more basic structures, work to understand the relationship between the structures and analyze how the structural pieces work together to create meaning. NLP Engineers use these linguistic tools to engineer computers that perform useful tasks involving human language.

Amazon’s Alexa and Apple’s Siri are common examples of how NLP engineering has changed lives, as people have come to rely on these virtual assistants to complete many different tasks throughout the day.

What We Need Your Help With

  • Engineer devices that understand human language and complete an action.
  • Creating computers that analyze, comprehend and generate human language, including speech functions.
  • As a component of AI, build computer programs that understand spoken human language.

We Look For

  • A strong aptitude for mathematics, particularly algebra and statistics.
  • Someone skilled with data modeling and engineering structures.
  • A team player who thrives when facing challenges.
  • Someone who can identify and implement new technologies.

These Would Also Be Nice

  • Excellent communication skills
  • A desire to keep up with the latest technologies
  • Keen to build the most functional NLP structures

Senior NLP Engineer Career Paths: Where to Go from Here

NLP Engineers at the senior level are at the top of their career, exploring relatively uncharted areas to create complex and highly-functional actionable devices. At the senior level, an NLP Engineer may be happy staying where they are or more see it as an opportunity to evolve.

Senior NLP Engineers who wish to change their career at this point will likely do so in a similar field. Maybe they relish the research aspect of NLP Engineering and take on a role as a research manager at their organization, overseeing tech and research teams. Perhaps design was a favorite aspect of NLP Engineering and they decide to move into a full-stack engineering role, designing and developing software for the front and backend of applications.

Many NLP Engineers may be happy to stay on at the senior level for the course of their career, enjoying the various evolutions of the field and gaining further insights into NLP Engineering.

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