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. 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. 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.