Natural Language Processing Engineer job description

A Natural Language Processing Engineer specializes in developing algorithms and systems that enable computers to understand, interpret, and generate human language, driving innovation in AI-powered applications such as chatbots, translation services, and sentiment analysis tools.

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What is a Natural Language Processing Engineer?

A Natural Language Processing (NLP) Engineer is a specialized AI professional who focuses on bridging the gap between human communication and computer understanding. They design, build, and deploy systems that process and analyze large volumes of natural language data, leveraging techniques from computational linguistics, machine learning, and deep learning. NLP Engineers work on cutting-edge technologies like speech recognition, text generation, and language translation, ensuring that machines can interact with humans in intuitive and meaningful ways. Their expertise is critical in industries ranging from healthcare and finance to customer service and entertainment, where language-driven automation enhances efficiency and user experience.

What does a Natural Language Processing Engineer do?

NLP Engineers develop and optimize models for tasks such as text classification, named entity recognition, sentiment analysis, and machine translation. They preprocess and clean textual data, train machine learning models using frameworks like TensorFlow or PyTorch, and fine-tune pre-trained models such as BERT or GPT for specific applications. Additionally, they collaborate with data scientists and software engineers to integrate NLP solutions into products, conduct A/B testing to evaluate model performance, and stay updated with advancements in the field to implement state-of-the-art techniques. Their work ensures that AI systems can accurately understand context, detect nuances, and generate coherent responses.

Job Overview

We are seeking a highly skilled Natural Language Processing Engineer to design, develop, and deploy cutting-edge NLP solutions. The ideal candidate will leverage state-of-the-art techniques to solve complex language understanding problems, build scalable NLP pipelines, and contribute to our AI-driven products serving US markets. This role requires deep expertise in machine learning, linguistic processing, and software engineering to create impactful language technologies.

Natural Language Processing Engineer responsibilities include:

1. Design and implement end-to-end NLP pipelines for text processing, entity recognition, sentiment analysis, and semantic understanding 2. Develop and fine-tune transformer-based models (BERT, GPT, T5) for specific domain applications 3. Build scalable text classification, information extraction, and text generation systems 4. Optimize NLP models for production deployment with focus on latency and throughput 5. Collaborate with data engineers to create efficient data preprocessing and feature engineering pipelines 6. Implement evaluation frameworks to measure model performance and business impact 7. Stay current with latest NLP research and incorporate advancements into production systems
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Must-Have Requirements

1. Master's degree in Computer Science, Computational Linguistics, or related field with 3+ years NLP experience 2. Strong programming skills in Python and experience with NLP libraries (spaCy, NLTK, Hugging Face) 3. Deep understanding of transformer architectures and experience with PyTorch/TensorFlow 4. Proven experience deploying NLP models in cloud environments (AWS, GCP, Azure) 5. Solid foundation in machine learning fundamentals and statistical modeling 6. Experience with text preprocessing, tokenization, and feature extraction techniques 7. US work authorization and ability to work in fast-paced technology environment

Preferred Qualifications

1. PhD in Computational Linguistics, Computer Science, or related field 2. Experience with large-scale distributed training and model optimization 3. Background in multilingual NLP or cross-lingual transfer learning 4. Familiarity with knowledge graphs, semantic web technologies, or ontology development 5. Experience with speech processing or conversational AI systems 6. Publications in top NLP conferences (ACL, EMNLP, NAACL) 7. Experience with MLOps tools and practices for NLP systems

Bonus Skills

1. Experience with low-resource language processing or few-shot learning 2. Background in healthcare NLP, legal text processing, or financial document analysis 3. Contributions to open-source NLP projects or libraries 4. Experience with quantum NLP or novel computational approaches 5. Patent filings in NLP or machine learning technologies 6. Experience with ethical AI practices and bias mitigation in NLP systems 7. Multilingual proficiency relevant to US market applications (Spanish, Chinese, etc.)

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