Large Language Modelling/GenAI Engineer

<p>Duties include:<br>• Model training and optimization: Design and implement large language model training strategies, including supervised fine-tuning (SFT), reinforcement learning (such as GRPO, PPO), etc., to enhance the model's intelligence in the Web3 field.<br>• Data processing and generation: Build a high-quality training dataset, perform data distillation and Long&Short Chain of Thought (Long&Short Chain of Thought, CoT) data generation to ensure that the model has strong inference abilities.<br>• Model architecture and evaluation: Explore and apply advanced model architectures such as expert mixing (MoE), develop model evaluation frameworks and metrics, and continuously optimize model performance.<br>• Distributed training and deployment: Develop and maintain distributed training schemes for models to ensure efficient training and stable deployment. <br>• Technological frontier exploration: Track the latest research dynamics in the AI field, such as OpenAi GPT-4.5, DeepSeek-R1, etc., and promote technological innovation and application in actual business. <br>Position requirements<br>• Educational background: Bachelor's degree in Computer Science, Artificial Intelligence, Machine Learning or related fields, with preference for Master's or Doctoral degrees.<br>• Technical skills:<br>• Proficient in Transformer architecture, familiar with Transformer Reinforcement Learning (TRL), PyTorch or TensorFlow deep learning-based learning frameworks, etc.<br>• Have large language model fine-tuning experience, familiar with reasoning-oriented reinforcement learning (Reasoning-Oriented Reinforcement Learning, RORL) technology. <br>• Familiar with distributed training frameworks, with practical experience in model parallelism, Flash Attention, LoRA, etc.,<br>• Engineering capability:<br>• Proficient in Python, Go, etc. programming language, with good coding style and software engineering practical experience.<br>• Familiar with model serving technologies such as Triton, vLLM, TGI, etc., those with inference optimization experience are preferred.<br>• Research ability:<br>• Able to read and implement cutting-edge papers, write technical reports or blogs.<br>• Priority will be given to those with papers published or open-source project contributions at top conferences (such as NeurIPS, ICLR, ICML, ACL).<br>• Soft skills:<br>• Have excellent team collaboration and communication skills, and be able to work efficiently with cross-functional teams.<br>• In-depth understanding of open-source AI communities, with contributors to relevant projects being preferred.</p>