Natural Language Processing (NLP) Engineers

Hire Natural Language Processing (NLP) Engineers

Natural language processing (NLP) is an interdisciplinary field of computer science that combines elements of information science, artificial intelligence (AI), and linguistics in order to study the interaction between computers and human language. NLP is a vital component of many technologies, including search engines, machine translation, automated dialogue systems, natural language interfaces, and text analytics applications. The objective of NLP is to enable computers to understand and process natural language, thereby facilitating communication and data processing between humans and machines.

Computers are highly adept at organising data, but they require assistance when handling human languages. Every language and regional variation has its own set of specific grammar regulations, slang expressions, specialised terminology, and sentence structure.

Have you ever pondered how Google or Alexa are capable of interpreting what you are saying? This is an example of the power of Natural Language Processing (NLP)! NLP Engineers are responsible for creating the code that enables technology to comprehend and analyse natural language input. Consequently, NLP Engineers play an essential role in enabling machines to understand and interact with humans in a more human-like manner.

Due to its widespread usage, Natural Language Processing (NLP) is a popular choice for businesses that are starting a web development initiative. Developers with prior experience in NLP technologies are highly sought after, so if you are contemplating on pursuing remote Natural Language Processing developer employment, you have a range of opportunities available to you.

What is the scope of development for Natural Language Processing?

As the amount of available data continues to grow and algorithms become more intricate and precise, Natural Language Processing (NLP) is becoming increasingly popular. This technology is having a major impact on the way humans and machines interact with each other. The numerous applications of NLP demonstrate that it can significantly enhance our quality of life.

It is estimated that up to eighty percent of all the data we encounter is unstructured, making Natural Language Processing (NLP) one of the most important topics within the field of data science. Researchers are continually striving to develop better methods for organising this material and as a result, NLP is rapidly evolving and is likely to become increasingly prevalent in all aspects of our lives in the future.

Are you intrigued by the prospect of applying for remote Natural Language Processing (NLP) engineer roles? To gain a better understanding of the tasks and responsibilities associated with these positions, let us delve a little deeper.

What are the duties and obligations of a Natural Language Processing (NLP) engineer?

Natural Language Processing (NLP) Engineers work collaboratively with a group of expert engineers to create and construct the next version of a company’s mobile applications. Further, other teams concerning app development and technical matters are working in tandem with the developers to manufacture the product.

Following the acquisition of remote Natural Language Processing (NLP) engineer positions, a developer’s primary duties include designing and developing NLP systems. This involves creating, testing, and deploying algorithms to process natural language data, and developing software applications that use the output of these algorithms. Additionally, the developer will be responsible for maintaining, optimising, and troubleshooting existing NLP systems, as well as researching new technologies and techniques to improve the performance of NLP systems.

  • Define appropriate language learning datasets.
  • Use strong text representations to transform natural language into important traits.
  • Create NLP systems that adhere to standards.
  • Experiment and train the constructed model.
  • Find and apply the appropriate algorithms and tools for NLP work.
  • Improve the models by statistically analysing the data.
  • Maintain a consistent degree of understanding in machine learning.
  • Keep NLP frameworks and libraries up to date.
  • Changes should be implemented as required, and defects should be investigated.

How does one go about becoming a Natural Language Processing (NLP) engineer?

Becoming a Natural Language Processing (NLP) engineer does not require any specific academic credentials. It is possible to learn the necessary skills and create a career out of this field, regardless of educational background or skill level. What is important is the ability to gain practical experience, as well as having the technical and non-technical knowledge and skills needed to excel in this field.

Despite the fact that many positions in the field of Natural Language Processing (NLP) engineering require a bachelor’s or master’s degree in computer science or a related field, there are several advantages to having a degree that may not always be immediately apparent. Having a degree in a relevant field will provide a solid understanding of the technologies used in NLP engineering, as well as providing a competitive edge over other candidates due to the depth of knowledge it affords.

Let’s look at some of the abilities and techniques that might help you get a career as a Natural Language Processing (NLP) engineer.

Natural Language Processing (NLP) engineers must have certain skills.

The first step in obtaining high-paying Natural Language Processing (NLP) engineer employment is to develop the following essential skills.

  1. Processing of Text

    In order to be a successful remote Natural Language Processing (NLP) engineer, it is essential to become familiar with the fundamentals of text processing. This includes the ability to manipulate text, working with regular expressions, and string slicing. These skills are some of the most important ones to have when engaging in NLP, and having a good understanding of them will help you to become a sought-after engineer in the field. Therefore, take the time to learn more about text processing in order to be able to access the best remote NLP engineer jobs.
  2. The NLTK Library

    The Natural Language Toolkit Library (NLTK) is one of the oldest and most comprehensive Natural Language Processing libraries available. It was initially released over 20 years ago and continues to be one of the best resources for learning the fundamentals of NLP. The library is well-organised and contains a variety of tools, such as stemmers, tokenizers, part-of-speech taggers, word lemmatizers, and N-grammes. By understanding the NLTK library, users can gain the skills required to build an NLP pipeline from scratch. Additionally, knowledge of these tools can be beneficial for impressing recruiters for remote NLP engineer positions.
  3. Text Data Reading

    In recent years, the internet has experienced a massive surge in the amount of textual data that is being circulated. As an NLP practitioner, it is important to be able to navigate different kinds of data formats, such as CSV and JSON files, and extract the text data from them to begin working on a NLP application. In addition to this, data scientists must also be able to retrieve text data from the internet. Thus, it is essential to have a strong understanding of how to collect and read text data from a variety of sources.
  4. Word Graphs

    Word vectors are a key component of modern Natural Language Processing (NLP) and can provide a valuable insight into how Artificial Neural Networks are used in this field. It is essential to understand and research the majority of Word Vectors, not only for NLP, but also for Machine Learning as a whole. By exploring this subject matter, developers can gain an understanding of the inner workings of Neural Networks, which are one of the most prominent models used in Machine Learning today. This includes an exploration into the fundamentals of backpropagation, weight optimisation, activation functions and gradient descent, thus providing a strong foundation for the development and execution of a variety of Neural Network models. As such, it is no surprise that technical recruiters during the remote NLP engineer job hiring process will closely examine an engineer’s understanding of Word Vectors, as well as how they have been employed in previous projects.
  5. Recurrent Neural Networks (RNNs)

    Recent developments in Natural Language Processing have enabled significant progress in the field of text production, due to the implementation of Neural Networks. Unlike Neural Networks used for Word Vectorization and Text Classification, Recurrent Neural Networks (RNNs) have been specifically designed to store and modify data that is linked sequentially, such as sentences and phrases. These distinct types of Neural Networks provide a variety of techniques for tackling the challenge of text production.

How can I get work as a remote Natural Language Processing (NLP) engineer?

Successful athletes and Natural Language Processing (NLP) engineers share a lot of characteristics and habits. To reach peak performance in their respective fields, both must devote time and energy to regular and effective practice. An NLP engineer should take advantage of the expertise of a successful NLP specialist to maximise the effectiveness of their practice sessions. Furthermore, a knowledgeable NLP engineer should be aware of the signs of burnout and be mindful of the amount of practice they should be doing. Ultimately, to be successful in either field, one must invest time in effective practice and be conscious of the risks of burnout.

At Works, we strive to provide the best remote Natural Language Processing (NLP) engineer jobs that will help you progress in your career as a NLP engineer. Our platform enables you to work on challenging technical and business issues while leveraging innovative technology, allowing you to quickly hone your skills. Joining our network of the world’s foremost NLP engineers gives you the opportunity to secure full-time, long-term remote NLP engineer jobs with higher compensation and professional development.

Job Description

Responsibilities at work

  • Choose suitable annotated datasets for supervised learning algorithms.
  • To convert natural language into usable characteristics, employ good text representations.
  • Find and use the best algorithms and tools for NLP jobs.
  • Create NLP systems in accordance with the specifications.
  • Run assessment experiments and train the created model.
  • Conduct statistical analysis and model refinement
  • Extend machine learning libraries and frameworks for use in NLP jobs.
  • Keep up with the fast developing area of AI and ML.


  • Bachelor’s/Master’s degree in engineering, computer science, or information technology (or equivalent experience)
  • 3+ years of NLP or Machine Learning engineering experience (rare exceptions for highly skilled developers)
  • Knowledge of NLP methods and algorithms is required.
  • The ideal candidate should possess an in-depth understanding of text representation, semantic extraction methods, data structures, and modelling. Additionally, they should possess a working knowledge of back-end technologies such as Python, Java, and R.
  • Working understanding of machine learning frameworks and libraries (such as Keras or PyTorch) is required.
  • Knowledge of large data frameworks such as Spark and Hadoop
  • Text representation methods, statistics, and classification algorithms are all required.
  • To communicate successfully, you must be fluent in English.
  • Working full-time (40 hours per week) with a 4-hour overlap with US time zones

Preferred skills

  • Knowledge of machine translation and compilation
  • A comprehensive understanding of Continuous Integration/Continuous Delivery (CI/CD) pipelines and the ability to analyse and interpret both the syntax and semantics of code is required. Additionally, strong analytical and interpersonal skills are needed to be successful in creating reliable and thoroughly tested code.
  • Ability to work both individually and as part of a team


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What makes Works Natural Language Processing (NLP) Engineers different?
At Works, we maintain a high success rate of more than 98% by thoroughly vetting through the applicants who apply to be our Natural Language Processing (NLP) Engineer. To ensure that we connect you with professional Natural Language Processing (NLP) Engineers of the highest expertise, we only pick the top 1% of applicants to apply to be part of our talent pool. You'll get to work with top Natural Language Processing (NLP) Engineers to understand your business goals, technical requirements and team dynamics.