About the role
Senior Applied Scientist, Large Language Models
We are looking for a Senior Applied Scientist, Large Language Models to advance the LLM capabilities powering Patsnap’s AI products for complex, knowledge-intensive work. You will work across applied research, model development, post-training, evaluation, and production deployment, tackling challenges in areas such as reasoning, long-context understanding, retrieval-augmented generation, information extraction, and domain adaptation. This role combines deep algorithmic expertise with a strong product mindset, translating emerging AI research into reliable, scalable, and measurable capabilities that deliver real-world value to Patsnap’s customers.
You will collaborate closely with engineering, product, data, and domain experts to identify high-impact problems, develop and rigorously evaluate solutions, and take them from experimentation through to production. As a senior technical contributor, you will also help shape best practices, guide other researchers and engineers, and contribute to the longer-term evolution of Patsnap’s AI technology roadmap.
Want to see the platform you'd be representing? Check out this short overview:
This is a in office position in our Shanghai, China office.
Who are we?
Patsnap is a global, pre-IPO company that transforms the way organizations harness their Intellectual Property and Research & Development productivity. Our platform revolutionizes how IP and R&D teams collaborate across the entire innovation lifecycle, using domain-specific AI to accelerate the creation of market-ready products. With over 12,000 customers worldwide, including some of the biggest names in innovation, Patsnap is at the forefront of technological advancement. Our $300M Series E funding round brings our valuation to a $1 billion unicorn status, and we still have a remarkable amount of growth ahead.
We have a vibrant and diverse team with offices in Singapore, Toronto, London, Shanghai and remote teams based in US. Our hyper-growth trajectory is powered by our people, and we are extremely proud of our company-wide vision, work ethic, and entrepreneurial spirit. We are committed to fostering an inclusive environment where talent thrives and ideas bloom.
What You'll Be Doing:
- Research and develop large language model capabilities for real-world applications.
- Improve model performance in areas such as reasoning, long-context understanding, information extraction, retrieval-augmented generation, and domain adaptation.
- Design and implement model post-training approaches, including supervised fine-tuning, preference optimization, knowledge distillation, and synthetic data generation.
- Develop systematic evaluation methodologies covering accuracy, factuality, robustness, safety, latency, and cost.
- Build scalable data preparation, model experimentation, and evaluation pipelines.
- Analyse model failure cases and identify effective approaches for continuous improvement.
- Explore emerging research and assess its practical value in production environments.
- Work with engineering teams to deploy and optimize models and AI capabilities in production.
- Collaborate with product managers and domain experts to translate business requirements into algorithmic solutions.
- Contribute to technical standards, best practices, and the longer-term development of the AI technology roadmap.
- Provide technical guidance and support to other algorithm engineers and researchers.
Requirements
What We'd Love From You:
- Master’s degree or PhD in Computer Science, Artificial Intelligence, Machine Learning, Natural Language Processing, or a related discipline, or equivalent practical experience.
- Strong experience in machine learning, natural language processing, or applied AI.
- Hands-on experience developing or adapting large language models.
- Strong understanding of Transformer architectures, model training, fine-tuning, and inference.
- Practical experience in at least two of the following areas:
- LLM post-training and alignment
- Model evaluation and benchmarking
- Retrieval-augmented generation
- Long-context modelling
- Information extraction
- Complex reasoning and planning
- Model compression or inference optimization
- Strong proficiency in Python and deep learning frameworks such as PyTorch.
- Ability to independently define algorithmic problems, design experiments, analyse results, and deliver production-ready solutions.
- Strong communication and cross-functional collaboration skills.
Preferred Qualifications:
- Experience developing AI solutions for enterprise, scientific, technical, or other knowledge-intensive applications.
- Experience with distributed training, large-scale inference, or GPU optimization.
- Experience building automated evaluation systems, data flywheels, or human-feedback pipelines.
- Experience with multimodal models, AI agents, or tool-augmented language models.
- Publications in reputable AI, machine learning, or NLP conferences, or meaningful open-source contributions.
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