NLP Engineer

4 weeks ago

Austin, Texas, United States Unreal Gigs Full time
Job Title: NLP Engineer - Language Architect

Are you passionate about teaching machines to understand, interpret, and generate human language? Do you thrive on building advanced language models that can extract meaning from vast amounts of text, power conversational agents, and transform natural language data into actionable insights? If you're excited about shaping the future of human-computer interaction through Natural Language Processing (NLP), then our client has an exciting opportunity for you.

As an NLP Engineer at our client, you'll develop sophisticated language models, work with large-scale datasets, and create solutions that improve text analysis, language generation, and understanding across various applications. Your expertise will power features like chatbots, voice assistants, sentiment analysis, and much more.

Key Responsibilities:
  1. Develop NLP Models and Algorithms:
  • Design and implement cutting-edge NLP models using machine learning and deep learning techniques. You'll work with architectures like transformers, recurrent neural networks (RNNs), and bidirectional encoder representations (BERT, GPT) to develop models for text classification, named entity recognition, sentiment analysis, and language generation.
Data Preprocessing and Feature Engineering:
  • Preprocess and clean large textual datasets, converting unstructured data into structured formats suitable for model training. You'll apply tokenization, stemming, lemmatization, and other NLP techniques to extract meaningful features from raw text.
Train and Fine-Tune Language Models:
  • Train, fine-tune, and optimize pre-trained language models like GPT, BERT, and T5 on custom datasets. You'll experiment with various model architectures and hyperparameters to improve accuracy, speed, and generalization.
Deploy NLP Solutions in Production:
  • Work with engineering teams to deploy NLP models into production environments. You'll ensure models are scalable, efficient, and integrated seamlessly into applications such as chatbots, recommendation engines, or voice assistants.
Model Evaluation and Performance Monitoring:
  • Evaluate model performance using metrics such as accuracy, F1-score, precision, recall, and perplexity. You'll monitor models in production and retrain or fine-tune them as needed to maintain performance over time.
Collaborate with Cross-Functional Teams:
  • Work closely with product managers, software engineers, and data scientists to understand business goals and integrate NLP solutions into products. You'll provide insights on linguistic challenges and propose solutions that align with business objectives.
Stay Updated on NLP Research and Trends:
  • Keep up with the latest advancements in NLP and deep learning. You'll experiment with new techniques like zero-shot learning, transfer learning, and transformer-based architectures to bring innovation into our client's NLP solutions.

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