职位概况:
1.Contribute to transitioning LLM or VLA models from research prototypes to our digital system and edge devices, enabling low-latency, high-reliability visual perception to monitor machines in field and action decision-making.
1.参与将前沿多模态大模型或VLA模型从实验室推向我们的远程数字中心系统
以及设备边缘设备，实现低延迟、高可靠的视觉理解以监测及控制现场设备。
2.Fine-tune, deploy, and optimize Multimodal Large Language Model (MLLM) or Vision-Language-Action (VLA) model.
2.多模态大模型或VLA模型的微调、部署和优化。
工作职责:
1.Deploy LLM on the digital system to monitor the real-time operational status of on-site equipment and provide instant feedback.
在数字中心系统上部署大模型以实时监控现场设备运行状态及实时反馈。
1.Responsible for the development, deployment, and optimization of Vision-Language-Action (VLA) or Multimodal Large Language Model (MLLM) applications.
负责视觉-语言-行动模型(VLA)和多模态大模型(MLLM)应用的开发、部署与优化工作。
2.Perform domain-specific fine-tuning of VLA/MLLM models to adapt them to targeted robotic tasks and operational scenarios, enhancing task-specific accuracy and robustness.
2.针对特定机器人任务和场景，对 VLA/MLLM 模型进行微调（Fine-tuning），提升模型在特定任务上的性能。
3.Document and maintain software architecture, API, and technical guides for development and support teams.
3.为开发和技术支持撰写并维护软件的相关文档。
岗位要求:
1.Bachelor's degree or higher, with basic English listening, speaking, reading, and writing skills.
本科及以上学历，具备基础的英语听说读写能力。
1.Proficient in Python Expertise in machine learning frameworks such as TensorFlow/PyTorch.
熟练掌握 Python，精通Tensorflow/PyTorch等机器学习框架。
2.In-depth knowledge of Transformer architectures (including attention mechanisms, positional encoding, and cross-modal fusion techniques).
深入了解Transformer架构（包括注意机制、位置编码和跨模态融合技术）。
2.Familiarity with VLA and MLLM architectures, with hands-on experience in training and fine-tuning multimodal large models.
熟悉 VLA\MLLM模型架构，具备多模态大模型训练与调优经验。
3.Familiarity with frameworks such as LangChain, LangGrap, and LlamaIndex; familiarity with large model-related technologies like RAG and MCP is preferred.
3.了解LangChain、LangGrap、LlamaIndex等框架，熟悉RAG、MCP等大模型相关技术优先。
4.Familiar with ROS/ROS2 robotics frameworks; preference will be given to candidates with hands-on experience deploying VLA models on real robotic platforms (e.g., NVIDIA Isaac GR00T) and successfully executing practical robotic tasks.
4.了解 ROS/ROS2 机器人操作系统，有在真实机器人平台（如NVIDIA Isaac GR00T）上部署 VLA 模型并完成实际任务的经验者优先。