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.
About the role
We are looking for machine learning engineers 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.
Responsibilities
Responsible for exploring NLP-related cutting-edge technologies and applying them to search senario and AI agent senario.
Responsible for analysing and understanding of the massive structured and unstructured data by NLP tasks, such as data processing, NER, relationship extraction, etc.
Responsible for developing and fine-tuning models to meet business requirements.
Work with a cross functional team to achieve business goals.
Requirements
Minimum Qualifications
Master or Bachelor degree in Computer Science, AI, or related field.
Excellent coding ability, data structure and basic algorithm foundation.
Proficient in C/C++ or Python.
Familiar with NLP-related algorithms and technologies.
Preferred Qualifications
Familiar with at least one field among ML, NLP, LLM, etc.
Experience in using deep learning frameworks like PyTorch & TensorFlow.
Experience in LLM, abstract, NER, or prompt engineering tuning, instruction, fine-tuning.
Experience in optimising pre-trained models, familiar with BERT and GPT-based papers published in ACL/AAAI/KDD/NIPS/CVPR.
Good communication and collaboration skills, able to explore new technologies with the team and promote technological progress.
Excellent problem analysis and problem-solving skills, able to deeply solve the problems existing in LLM.
