NLP Team Leader

Company Introduction

• PatSnap is a a SaaS-based tech innovation intelligence service provider. Patsnap helps R&D leaders to maximise the value of innovation intelligence within their R&D workflow and strategic planning. As the global leaders in connected innovation intelligence, PatSnap use AI-powered and machine learning technology to comb through billions of datasets, and help innovators connect the dots. Patsnap recently completed a Series E funding round of $300 million, led by Tencent and the SoftBank Vision Fund II. This represents the largest round of funding in the SaaS sector since 2020, Patsnap is a unicorn in the SaaS track.

Department Introduction

• The R&D SG Department is dedicated to building leading capabilities in natural language processing technology and services. This is achieved by leveraging technologies such as Deep Learning, NLP, CV, and Knowledge Graph to create top-tier products.

Responsibilities

• Utilise natural language processing, machine learning, and data mining technologies to build models to achieve business objectives
• Proficient in using NER and relationship extraction to solve business problems
• Study and transform various kind of structured and unstructured data
• Organising data in to usable formats
• Enhancing the data collection process
• Create visualisations of data
• Analysing unstructured data using basic NLP techniques and extract important fields
• Develop NLP systems according to requirements
• Perform statistical analysis of results and refine models
• Remain updated in the rapidly changing field of machine learning
• Deploy models, optimise inference speed, and processing big data

Requirements

• Minimum 5 years working experience in NLP and up-to-date with industry and academic progress
• Good knowledge in LLM
• Leadership skills to manage 4-5 engineers
• Fluent in English and Chinese
• Solid foundation in algorithms and have passion for technology development and application
• Proficient in programming languages such as Python, and has a good grasp of deep learning frameworks like PyTorch, and TensorFlow
• Familiar with name entity recognition and relationship extraction
• Experience in optimising pre-trained models, such as bert and gpt-based
• Papers published in ACL/AAAI/KDD/NIPS/CVPR and other conferences will be preferred