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.
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
We are looking for an experienced Senior Natural Language Processing
Engineer to help us improve various business outcomes and drive innovation.
You will join a multidisciplinary team helping to shape our Product development.
This is an excellent opportunity to take advantage of emerging trends and
technologies to a real-world difference.
- Familiar with at least one field among ML, NLP, Knowledge Graph,
- LLM， and up-to-date with industry and academic progress
- 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 gptbased
- papers published in ACL/AAAI/KDD/NIPS/CVPR and other conferences
- will be preferred
- 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
- 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