NLP Engineer

1 month ago

Singapur, Singapore RiceBowl Full time
Job Title: NLP Engineer

RiceBowl is seeking a highly skilled NLP Engineer to join our team. As an NLP Engineer, you will be responsible for developing and implementing advanced language models to enhance our organization's capabilities in understanding, processing, and generating human language.

Key Responsibilities:
  • Design, build, and implement NLP models and algorithms for tasks such as text classification, sentiment analysis, named entity recognition, language translation, and question-answering systems.
  • Develop pipelines for data cleaning, tokenization, lemmatization, and vectorization to preprocess large volumes of unstructured text data.
  • Identify and engineer relevant features from text data to improve model performance.
  • Train, fine-tune, and evaluate NLP models using frameworks like Hugging Face Transformers, TensorFlow, or PyTorch.
  • Collaborate with data scientists, software developers, and product teams to integrate NLP models into applications and services.
  • Stay current with advancements in NLP research, such as large language models (e.g., GPT, BERT), and apply new methods to improve existing products.
  • Deploy and maintain NLP models in production environments, ensuring scalability, performance, and reliability.
  • Monitor the performance of deployed models, performing updates and retraining as necessary to ensure continued effectiveness.
  • Document the development processes, code, models, and outcomes for future reference and reproducibility.
Qualifications:
  • Education: Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, Linguistics, or a related field. A Ph.D. is a plus.
  • Experience: 2+ years of experience in natural language processing, machine learning, or a related field.
  • Hands-on experience with NLP libraries and frameworks such as Hugging Face Transformers, spaCy, NLTK, or Gensim.
  • Experience with machine learning frameworks like TensorFlow or PyTorch.
  • Proficiency in programming languages such as Python or R.
  • Familiarity with deep learning techniques for NLP, such as attention mechanisms and transformer models.
Preferred Qualifications:
  • Experience with large-scale language models (e.g., BERT, GPT) and transfer learning in NLP.
  • Familiarity with distributed computing and big data technologies like Spark or Hadoop.
  • Experience with MLOps practices and tools for deploying and monitoring machine learning models.
  • Familiarity with tools such as Docker, Kubernetes, and CI/CD pipelines.
  • Knowledge of multilingual NLP models and their application in global contexts.

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