Senior Applied Scientist, Large Language Models

Location: Shanghai, China; on-site

Requirements

• 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, including hands-on development or adaptation of large language models.
• Strong understanding of Transformer architectures, model training, fine-tuning, and inference.
• Practical experience in at least two areas including LLM post-training and alignment, model evaluation, retrieval-augmented generation, long-context modelling, information extraction, complex reasoning, model compression, or inference optimization.
• Strong proficiency in Python and deep learning frameworks such as PyTorch.
• Ability to define algorithmic problems, design experiments, analyse results, and deliver production-ready solutions.
• Strong communication and cross-functional collaboration skills.

Nice to have

• Experience with enterprise, scientific, technical, or other knowledge-intensive AI 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.

What you will do

• Research and develop large language model capabilities for real-world, knowledge-intensive applications.
• Improve reasoning, long-context understanding, information extraction, retrieval-augmented generation, and domain adaptation.
• Design post-training approaches such as supervised fine-tuning, preference optimization, knowledge distillation, and synthetic data generation.
• Develop evaluation methodologies covering accuracy, factuality, robustness, safety, latency, and cost.
• Build scalable data preparation, experimentation, and evaluation pipelines; analyse failure cases and drive continuous improvement.
• Collaborate with engineering, product, data, and domain teams to deploy and optimize AI capabilities and contribute to technical standards and the AI roadmap.

Culture & Benefits

• Full-time, on-site work in Shanghai.
• Cross-functional collaboration with engineering, product, data, and domain teams.
• Opportunity to guide other algorithm engineers and researchers and contribute to long-term AI technology direction.