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職位概況:
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.
1.參與將前沿多模態(tài)大模型或VLA模型從實(shí)驗(yàn)室推向我們的遠(yuǎn)程數(shù)字中心系統(tǒng)
以及設(shè)備邊緣設(shè)備，實(shí)現(xiàn)低延遲、高可靠的視覺(jué)理解以監(jiān)測(cè)及控制現(xiàn)場(chǎng)設(shè)備。
2.Fine-tune, deploy, and optimize Multimodal Large Language Model (MLLM) or Vision-Language-Action (VLA) model.
2.多模態(tài)大模型或VLA模型的微調(diào)、部署和優(yōu)化。
工作職責(zé):
1.Deploy LLM on the digital system to monitor the real-time operational status of on-site equipment and provide instant feedback.
在數(shù)字中心系統(tǒng)上部署大模型以實(shí)時(shí)監(jiān)控現(xiàn)場(chǎng)設(shè)備運(yùn)行狀態(tài)及實(shí)時(shí)反饋。
1.Responsible for the development, deployment, and optimization of Vision-Language-Action (VLA) or Multimodal Large Language Model (MLLM) applications.
負(fù)責(zé)視覺(jué)-語(yǔ)言-行動(dòng)模型(VLA)和多模態(tài)大模型(MLLM)應(yīng)用的開(kāi)發(fā)、部署與優(yōu)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.針對(duì)特定機(jī)器人任務(wù)和場(chǎng)景，對(duì) VLA/MLLM 模型進(jìn)行微調(diào)（Fine-tuning），提升模型在特定任務(wù)上的性能。
3.Document and maintain software architecture, API, and technical guides for development and support teams.
3.為開(kāi)發(fā)和技術(shù)支持撰寫(xiě)并維護(hù)軟件的相關(guān)文檔。
崗位要求:
1.Bachelor's degree or higher, with basic English listening, speaking, reading, and writing skills.
本科及以上學(xué)歷，具備基礎(chǔ)的英語(yǔ)聽(tīng)說(shuō)讀寫(xiě)能力。
1.Proficient in Python Expertise in machine learning frameworks such as TensorFlow/PyTorch.
熟練掌握 Python，精通Tensorflow/PyTorch等機(jī)器學(xué)習(xí)框架。
2.In-depth knowledge of Transformer architectures (including attention mechanisms, positional encoding, and cross-modal fusion techniques).
深入了解Transformer架構(gòu)（包括注意機(jī)制、位置編碼和跨模態(tài)融合技術(shù)）。
2.Familiarity with VLA and MLLM architectures, with hands-on experience in training and fine-tuning multimodal large models.
熟悉 VLA\MLLM模型架構(gòu)，具備多模態(tài)大模型訓(xùn)練與調(diào)優(yōu)經(jīng)驗(yàn)。
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等大模型相關(guān)技術(shù)優(yōu)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 機(jī)器人操作系統(tǒng)，有在真實(shí)機(jī)器人平臺(tái)（如NVIDIA Isaac GR00T）上部署 VLA 模型并完成實(shí)際任務(wù)的經(jīng)驗(yàn)者優(yōu)先。