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Humanoid Robot Large-Model Application Engineer
Adapt and deploy large language models (LLMs) and vision-language models (VLMs) on humanoid robots, focusing on natural human-robot interaction, scene understanding and task planning.
Responsibilities
- Adapt and deploy LLMs and VLMs for humanoid robots, focusing on natural human-robot interaction, scene understanding and task planning
- Fine-tune, deploy and optimize large models for our humanoid prototypes, improving inference speed and accuracy for on-robot (edge) deployment
- Work with the motion-control and computer-vision teams to integrate large models with motion control and perception, strengthening autonomous decision-making and flexible interaction
- Write technical documents and test reports for large-model work, and explore new LLM + VLM applications on humanoid robots
- Follow advances in large models and vision-language fusion, and explore new techniques for the project to make our robots smarter
Requirements
- Master’s degree or above in mathematics, physics, computer science, AI, intelligent science, data science, automation or a related field
- Strong interest in LLMs, VLMs and their use on humanoid robots; a grounding in human-robot interaction and autonomous decision-making is a plus
- Solid computer-science fundamentals, Python, and a grasp of machine-learning and deep-learning basics
- Rigorous logical thinking, strong problem-solving, good cross-team collaboration and communication, and quick adaptation to project needs
- Familiarity with at least one large-model framework or model (e.g. Hugging Face, LangChain, LLaMA, ChatGLM); fine-tuning or prompt-engineering experience is a plus
- Understanding of vision-language models such as CLIP and BLIP, and able to build and debug simple vision-language tasks
- Deep-learning fundamentals including CNNs and Transformers, and able to train and deploy simple models with PyTorch or TensorFlow
- A basic understanding of how humanoid robots work; the ability to propose large-model adaptation and optimization for real use cases is a plus
Nice to have
- Strong coursework in machine learning, deep learning, NLP, computer vision, introduction to AI, and data structures and algorithms
- Projects or coursework on large-model fine-tuning and deployment, or on vision-language fusion
- Familiarity with ROS, and able to build large-model interaction modules on it
- Participation in AI or big-data competitions, or related academic publications
How to apply
Send your CV to ying.wan@china-lsh.com
Location
2F, Tianshengda Science and Technology Innovation Center, No. 118 Litai Road, Lihu Subdistrict, Binhu District, Wuxi, Jiangsu 214073, China
Posted
Apply →2026-10-12