Natural Language Processing Engineer

Job description

This Natural Language Processing Engineer Job Description template is tailored to suit the needs of your organization and attract highly skilled professionals. We have modified the template to highlight the key responsibilities and requirements for the role, ensuring that it appeals to talented developers

Hire Natural Processing Engineer
a man sitting on a bean bag with a laptop and a cup of coffee. a man sitting on a bean bag with a laptop and a cup of coffee.
an image of a white striped background swift icon in a circle

Job brief

Here is a job description for a Natural Language Processing Engineer

We are seeking a highly skilled and experienced Natural Language Processing (NLP) Engineer to join our innovative team. As an NLP Engineer, you will be responsible for developing and implementing cutting-edge algorithms and models to process and understand human language. You will collaborate with data scientists, software engineers, and domain experts to build robust NLP solutions that extract insights from textual data and enable advanced language-based applications. The ideal candidate will have a strong background in NLP, machine learning, and programming, along with a passion for solving complex language-related challenges.


  • Design, develop, and implement NLP algorithms, models, and techniques to extract meaning, sentiment, intent, and other linguistic features from textual data.
  • Collaborate with cross-functional teams to define project requirements and objectives, and translate them into technical solutions.
  • Collect, preprocess, and analyze large volumes of text data to train and evaluate NLP models, leveraging machine learning techniques.
  • Develop and optimize machine learning models for tasks such as text classification, named entity recognition, sentiment analysis, question answering, and information extraction.
  • Conduct research and stay updated with the latest advancements in NLP and machine learning, identifying opportunities for innovation and improvement.
  • Perform feature engineering, feature selection, and hyperparameter tuning to enhance model performance and accuracy.
  • Implement and deploy NLP solutions into production systems, ensuring scalability, reliability, and maintainability.
  • Collaborate with software engineers to integrate NLP components into larger software systems or platforms.
  • Conduct experiments and evaluations to assess the performance and effectiveness of NLP models and algorithms, iterating and refining as needed.
  • Stay abreast of industry standards and best practices for data privacy, security, and ethical considerations in NLP applications.
  • Document research findings, methodologies, and technical specifications to facilitate knowledge sharing and collaboration.

Preffered Skills:

  • Excellent communication and collaboration skills, with the ability to effectively convey technical concepts to both technical and non-technical stakeholders.
  • Strong attention to detail and commitment to producing high-quality work.
  • Experience with cloud platforms and distributed computing frameworks (e.g., AWS, GCP, Spark) is a plus.
  • Publications or contributions to the machine learning research community are highly desirable.
  • Publications or contributions to the NLP or machine learning research community are highly desirable.


  • Master's or Ph.D. degree in Computer Science, Natural Language Processing, Machine Learning, or a related field. Equivalent practical experience will also be considered.
  • Proven experience as an NLP Engineer, Data Scientist, or a similar role, with a track record of successfully implementing NLP solutions and models.
  • Strong understanding of NLP concepts, techniques, and algorithms, including but not limited to text representation, word embeddings, sequence modeling, and deep learning architectures (e.g., LSTM, Transformer).
  • Proficiency in programming languages commonly used in NLP, such as Python and libraries like NLTK, spaCy, scikit-learn, TensorFlow, or PyTorch.
  • Experience in training and evaluating NLP models using large datasets and machine learning frameworks.
  • Familiarity with data preprocessing techniques, such as tokenization, stemming, lemmatization, and part-of-speech tagging.
  • Knowledge of statistical analysis, experimental design, and evaluation metrics for assessing model performance.
  • Strong problem-solving and analytical skills, with the ability to think critically and creatively to tackle complex NLP challenges.
  • Mentor and provide guidance to junior NLP team members, fostering their professional growth and development.