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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Check out this short overview:
This is a in office position in our Shanghai, China office.
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.
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Research and develop large language model capabilities for real-world applications.
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Improve model performance in areas such as reasoning, long-context understanding, information extraction, retrieval-augmented generation, and domain adaptation.
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Design and implement model post-training approaches, including supervised fine-tuning, preference optimization, knowledge distillation, and synthetic data generation.
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Develop systematic evaluation methodologies covering accuracy, factuality, robustness, safety, latency, and cost.
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Build scalable data preparation, model experimentation, and evaluation pipelines.
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Analyse model failure cases and identify effective approaches for continuous improvement.
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Explore emerging research and assess its practical value in production environments.
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Work with engineering teams to deploy and optimize models and AI capabilities in production.
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Collaborate with product managers and domain experts to translate business requirements into algorithmic solutions.
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Contribute to technical standards, best practices, and the longer-term development of the AI technology roadmap.
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Provide technical guidance and support to other algorithm engineers and researchers.
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Master’s degree or PhD in Computer Science, Artificial Intelligence, Machine Learning, Natural Language Processing, or a related discipline, or equivalent practical experience.
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Strong experience in machine learning, natural language processing, or applied AI.
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Hands-on experience developing or adapting large language models.
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Strong understanding of Transformer architectures, model training, fine-tuning, and inference.
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Practical experience in at least two of the following areas:
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LLM post-training and alignment
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Model evaluation and benchmarking
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Retrieval-augmented generation
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Long-context modelling
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Information extraction
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Complex reasoning and planning
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Model compression or inference optimization
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Strong proficiency in Python and deep learning frameworks such as PyTorch.
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Ability to independently define algorithmic problems, design experiments, analyse results, and deliver production-ready solutions.
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Strong communication and cross-functional collaboration skills.
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Experience developing AI solutions for enterprise, scientific, technical, or other knowledge-intensive applications.
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Experience with distributed training, large-scale inference, or GPU optimization.
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Experience building automated evaluation systems, data flywheels, or human-feedback pipelines.
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Experience with multimodal models, AI agents, or tool-augmented language models.
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Publications in reputable AI, machine learning, or NLP conferences, or meaningful open-source contributions.
