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We are looking for a Natural Language Processing Engineer to help us improve our NLP products and create new NLP applications.
NLP Engineer responsibilities include transforming natural language data into useful features using NLP techniques to feed classification algorithms. To succeed in this role, you should possess outstanding skills in statistical analysis, machine learning methods and text representation techniques.
Your ultimate goal is to develop efficient self-learning NLP applications.
A Natural Language Processing (NLP) Engineer is responsible for developing and implementing algorithms, models, and systems that enable computers to understand and process human language. On a day-to-day basis, an NLP Engineer may perform tasks such as:
Data preprocessing: Cleaning and preparing textual data for further analysis by removing noise, formatting text, and handling special characters.
Language modeling: Developing statistical or machine learning models to understand the structure and patterns of natural language. This involves tasks like building n-grams, hidden Markov models, or recurrent neural networks.
Named entity recognition: Designing and implementing algorithms to identify and extract entities such as names of people, organizations, locations, etc., from text.
Sentiment analysis: Creating models that determine the sentiment (positive, negative, neutral) expressed in text or social media posts.
Text classification: Building algorithms that automatically categorize and classify text into predefined categories, such as spam detection, topic classification, or sentiment analysis.
Machine translation: Developing models or systems for translating text from one language to another using techniques such as statistical machine translation or neural machine translation.
Speech recognition: Creating algorithms or systems that convert spoken language into written text by using techniques like acoustic modeling, language modeling, and deep learning.
Chatbot development: Designing and implementing conversational agents or virtual assistants that can understand and respond to human language queries using techniques like intent recognition and response generation.
Evaluating and improving models: Analyzing the performance of NLP models by conducting experiments, measuring accuracy, precision, and recall, and fine-tuning the models to achieve better results.
Research and staying up-to-date: Keeping up with the latest research in NLP by reading academic papers, attending conferences, and participating in online forums and communities.
Note that the specific tasks and responsibilities of an NLP Engineer may vary based on the organization, project requirements, and individual expertise.
SummaryPosted: Jul 26, 2022...Role Number:200405959
Are you interested in making an extensive and impactful contribution to safety in Apple user experiences using natural language processing and machine learning? We are looking for an exceptional talent with a strong foundation in machine learning, domain expertise in NLP, and the ability to turn ideas into action.
We are an applied R&D te...
About the job Natural Language Processing Engineer
One for all those working in the NLP space: NLP Engineer, NLP Data Scientist, Machine Learning Engineers with NLP focus...
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Tietronix Software seeks a Natural Language Processing (NLP) engineer in a team environment to create new NLP applications and improve existing NLP products in Houston, Texas. The goal is to develop efficient self-learning NLP applications:
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Essential Job Functions:• Build, i...