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Deeply participate in data processing, cleaning, and optimization for CodeLLM; scientifically improve, filter, and balance the data quality used in LLM pre-training and alignment to enhance the model’s code and agent capabilities during the Pretrain and SFT stages.\n2. Actively engage in improving Coding/SWE Agent capabilities through SFT (Supervised Fine-Tuning), RL (Reinforcement Learning), and building/scaling tools, Docker environments, and tasks; personally gain know-how on how to effectively train an agentic model.\n3. Deeply explore methods to enhance the model’s problem-solving ability, aiming for: 1) Nearly 100% accuracy on simple and medium-level algorithm problems; 2) Maximizing the solving rate of difficult algorithm problems (IOI/ICPC level), pushing the model’s intelligence upper bound.\n4. Improve user problem-solving rates and satisfaction on Code/SWE-related questions, making the model genuinely easy and pleasant to use.\nJob Requirements:\n1. Bachelor’s degree or above, preferably in Computer Science, Artificial Intelligence, Automation, Mathematics, or related fields.\n2. Solid programming foundation, deep understanding of data structures and algorithm design; proficient in one or more mainstream programming languages such as Python and C++; skilled in deep learning frameworks and libraries like PyTorch, TensorFlow, and Hugging Face.\n3. Rich hands-on experience in large model pre-training and alignment training, or experience in Code/SWE optimization is preferred.\n4. Publication in top-tier academic conferences or outstanding achievements in machine learning and AI fields are preferred.\n5. Algorithm competition award experience (e.g., ACM, IOI, NOI, Top Coder) is a plus.\n6. 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