Patsnap is hiring a

Senior NLP Engineer

About PatSnap
Patsnap empowers IP and R&D teams by providing better answers, so they can make faster decisions with more confidence. Founded in 2007, Patsnap is the global leader in AI-powered IP and R&D intelligence. Our domain-specific LLM, trained on our extensive proprietary innovation data, coupled with Hiro, our AI assistant, delivers actionable insights that increase productivity for IP tasks by 75% and reduce R&D wastage by 25%. IP and R&D teams collaborate better with a user-friendly platform across the entire innovation lifecycle. Over 15,000 companies trust Patsnap to innovate faster with AI, including NASA, Tesla, PayPal, Sanofi, Dow Chemical, and Wilson Sonsini.

Key Responsibilities

  • Develop and implement advanced NLP and machine learning models, includingLLMs, to enhance product capabilities.
  • Utilize NER and relationship extraction techniques to efficiently construct high-quality datasets.
  • Design and develop custom NLP systems for specific requirements and use cases.
  • Deploy and optimize ML models for improved inference speed and efficient large-scale data processing.
  • Implement and enhance search engines or retrieval-augmented generation systems.
  • Collaborate with cross-functional teams to solve complex problems and trace the cutting-edge NLP technologies.

Desired Qualifications

  • Advanced knowledge in at least one of the following areas: ML, NLP, Knowledge Graphs, or LLMs.
  • Strong foundation in algorithms and a passion for technological innovation.
  • Proficiency in Python and experience with deep learning frameworks such as PyTorch and TensorFlow.
  • Expertise in named entity recognition and relationship extraction techniques.
  • Demonstrated experience in optimizing pre-trained models, particularly BERT and GPT-based architectures.
  • Publications in top-tier conferences (e.g., ACL, AAAI, KDD, NeurIPS, CVPR) are highly desirable.
  • Master's degree in Computer Science, Computational Linguistics, or a related field; Ph.D. preferred.


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