Job Description
1. Deeply engage in the R&D of multimodal large model foundations for embodied intelligence, optimizing model accuracy and training efficiency;
2. Deeply engage in developing visual capabilities for multimodal large models, including visual-video-language alignment, visual token compression, and enhancing spatio-temporal understanding;
3. Deeply engage in fine-tuning multimodal large models for embodied scenarios, leveraging techniques such as SFT, instruction fine-tuning, DPO, and GRPO for optimization;
4. Build high-quality training and testing datasets for embodied intelligence scenarios, establishing efficient evaluation metrics and data cleaning/annotation tools.
1、深度參與面向具身智能的多模態大模型基座的研發工作,優化大模型的精度與訓練效率;
2、深度參與多模態大模型的視覺能力研發,包括視覺視頻語言對齊,視覺Token壓縮,時空間理解能力提升等工作;
3、深度參與多模態大模型在具身場景的微調,利用包括SFT, 指令微調,DPO, GRPO等技術進行優化;
4、面向具身智能場景,構建高質量的訓練與測試數據集,搭建高效的評測標準與清洗標注工具。
Requirements
1. Master the theoretical foundations and algorithms of artificial intelligence, pattern recognition, and deep learning;
2. Understand large-scale model technologies; preference given to candidates with publications in top-tier journals/conferences such as CVPR, ICCV, ICLR, ICML, and NeurIPS;
3. Proficient in C/C++ and Python programming, with familiarity in Linux and ROS systems;
4. Possess strong teamwork and communication skills, along with excellent learning capabilities.
1.掌握人工智慧、模式識別、深度學習理論基礎和演算法;
2.瞭解大模型相關技術,在CVPR,ICCV,ICLR,ICML,NeruIPS等TOP期刊會議發表論文者優先;
3.熟悉C/C++和Python編程,熟悉Linux系統和ROS系統;
4.具備良好的團隊協作能力和溝通能力,具備良好的學習能力。