Senior Applied Scientist, Large Language Models

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.

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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.

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.